Code import - branch 0.5.0

laborious/activities/activities.py

@@ -0,0 +1,169 @@
+from sientia_do.observability.metrics_controller import MetricsController
+from temporalio import workflow
+
+with workflow.unsafe.imports_passed_through():
+    from typing import Any
+
+    from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
+    from sientia_do.observability.logger import Logger
+    from sientia_do.repository.minio_repository import MinioRepository
+
+    from laborious.activities.api import API
+    from laborious.activities.gates import Gates
+    from laborious.activities.mlflow import MLFlow
+    from laborious.activities.model_metrics import ModelMetrics
+    from laborious.activities.opc import OPC
+    from laborious.activities.storage import Storage
+
+
+class Activities(Storage, MLFlow, Gates, OPC, ModelMetrics, API):
+    """
+    Main activities orchestrator for the Laborious system.
+
+    This class combines functionality from multiple activity classes to provide
+    a unified interface for all workflow operations. It manages database connections,
+    MLFlow model interactions, data quality validation, and OPC server communications.
+
+    The class implements multiple inheritance to combine specialized functionality:
+    - Storage: Database operations and data persistence
+    - MLFlow: Model inference and transformation operations
+    - Gates: Data quality validation and filtering mechanisms
+    - OPC: Real-time data export to OPC servers
+    - ModelMetrics: Model performance metrics and drift detection
+    - API: PI Web API export operations for industrial systems
+
+    Attributes:
+        postgres_config (dict): PostgreSQL connection configuration
+        mlflow_config (dict): MLFlow server configuration
+        opc_config (dict): OPC server configuration
+        pi_web_api_config (dict): PI Web API server configuration
+        logger (Logger): Logging and observability instance
+        notification_handler (NotificationHandler): Notification management instance
+    """
+
+    def __init__(
+        self,
+        postgres_config: dict[str, Any],
+        mlflow_config: dict[str, Any],
+        minio_config: dict[str, Any],
+        opc_config: dict[str, Any],
+        pi_web_api_config: dict[str, Any],
+        logger: Logger,
+        notification_handler: NotificationHandler,
+    ):
+        """
+        Initialize the Activities orchestrator with all required configurations.
+
+        This constructor initializes all parent classes with their respective
+        configurations and sets up the foundation for all activity operations.
+
+        Args:
+            postgres_config: PostgreSQL connection configuration dictionary
+                Required keys: host, port, user, password, dbname, min_connections, max_connections
+            mlflow_config: MLFlow server configuration dictionary
+                Required keys: host, port, username, password
+            opc_config: OPC server configuration dictionary
+                Can contain multiple server configurations
+            pi_web_api_config: PI Web API server configuration dictionary
+                Required keys: base_url, auth_type, auth_token
+            logger: Logger instance for observability and debugging
+            notification_handler: Notification handler for alerts and monitoring
+
+        Raises:
+            Exception: If any parent class initialization fails
+        """
+        metrics_controller = MetricsController(logger=logger)
+
+        minio_repository = MinioRepository(
+            endpoint=minio_config['endpoint_url'],
+            access_key=minio_config['access_key'],
+            secret_key=minio_config['secret_key'],
+            bucket=minio_config['default_bucket'],
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
+            secure=minio_config['secure'],
+        )
+
+        # Initialize parent classes
+        Storage.__init__(
+            self,
+            host=postgres_config['host'],
+            port=postgres_config['port'],
+            user=postgres_config['user'],
+            password=postgres_config['password'],
+            dbname=postgres_config['dbname'],
+            min_connections=postgres_config['min_connections'],
+            max_connections=postgres_config['max_connections'],
+            retention_hours=minio_config['retention_hours'],
+            minio_repository=minio_repository,
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
+        )
+
+        MLFlow.__init__(
+            self,
+            mlflow_host=mlflow_config['host'],
+            mlflow_port=mlflow_config['port'],
+            mlflow_username=mlflow_config['username'],
+            mlflow_password=mlflow_config['password'],
+            minio_repository=minio_repository,
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
+        )
+
+        Gates.__init__(
+            self,
+            minio_repository=minio_repository,
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
+        )
+
+        OPC.__init__(
+            self,
+            opc_servers=opc_config,
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
+        )
+
+        ModelMetrics.__init__(
+            self,
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
+        )
+
+        API.__init__(
+            self,
+            base_url=pi_web_api_config['base_url'],
+            auth_type=pi_web_api_config['auth_type'],
+            auth_token=pi_web_api_config['auth_token'],
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
+        )
+
+    async def shutdown(self):
+        """
+        Gracefully shutdown all activities and clean up resources.
+
+        This method ensures proper cleanup of all resources including:
+        - PostgreSQL connection pools
+        - OPC server connections
+        - PI Web API client connections
+        - MLFlow model repositories
+        - Any other resources that need explicit cleanup
+
+        The method should be called before the application terminates to ensure
+        proper resource cleanup and prevent resource leaks.
+        """
+        Storage.close(self)
+        MLFlow.close(self)
+        Gates.close(self)
+        await OPC.close(self)
+        ModelMetrics.close(self)
+        API.close(self)

laborious/activities/api.py

@@ -0,0 +1,305 @@
+from temporalio import activity, workflow
+
+with workflow.unsafe.imports_passed_through():
+    import json
+    import traceback
+    from typing import Any
+
+    from pandas import DataFrame
+    from sientia_do.notifications.handlers import NotificationHandler
+    from sientia_do.notifications.models import NotificationLevel
+    from sientia_do.observability.logger import Logger
+    from sientia_do.observability.metrics_controller import MetricsController
+    from sientia_do.observability.sientia_monitoring import SientiaMonitoring
+    from sientia_do.repository.pi_web_api_client import PIWebAPIClient
+
+    from laborious import metrics
+
+
+PI_WEB_API_PREDICTION_ERROR_CONFIDENCE = 13
+
+
+class API(SientiaMonitoring):
+    """
+    PI Web API operations for writing prediction data to PI Web API.
+
+    This class provides Temporal activities for interacting with the PI Web API
+    to write prediction and confidence values to industrial systems. It handles
+    error scenarios gracefully by setting error confidence values and sending
+    notifications when write operations fail.
+
+    The class implements comprehensive error handling for both prediction and
+    confidence value writes, ensuring that partial failures are properly
+    reported and handled.
+    """
+
+    def __init__(
+        self,
+        base_url: str,
+        auth_type: str,
+        auth_token: str,
+        logger: Logger,
+        notification_handler: NotificationHandler,
+        metrics_controller: MetricsController,
+    ) -> None:
+        """
+        Initialize API activity with PI Web API client.
+
+        Args:
+            base_url (str): Base URL of the PI Web API server
+            auth_type (str): Authentication type ('basic' or 'bearer')
+            auth_token (str): Authentication token
+            logger (Logger): Logger instance for operation logging
+            notification_handler (NotificationHandler): Handler for system notifications
+            metrics_controller (MetricsController): Controller for metrics collection
+        """
+        SientiaMonitoring.__init__(self, logger, notification_handler, metrics_controller)
+        self.pi_web_api_client = PIWebAPIClient(
+            base_url=base_url,
+            auth_config={
+                'type': auth_type,
+                'token': auth_token,
+            },
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
+            headers_config={
+                'Content-Type': 'application/json',
+                'Accept': 'application/json',
+                'x-requested-with': 'piwebapistreams',
+                'User-Agent': 'Aig-Laborious-Agent/1.0',
+            },
+        )
+
+    def get_pi_web_api_core_labels(
+        self,
+        metadata: dict[str, Any],
+        operation_type: str = 'write_pi_web_api_data',
+    ) -> dict[str, Any]:
+        """
+        Generate core labels for PI Web API metrics.
+
+        PI Web API metrics in laborious use the shared ``CORE_LABELS`` from
+        ``sientia_do``, which includes ``operation_type``. For this reason,
+        operation_type must always be present in emitted labels.
+
+        Args:
+         - metadata (dict[str, Any]): Workflow execution metadata used to derive labels.
+         - operation_type (str): Operation type label for metric cardinality.
+
+        Return:
+         dict[str, Any]: Core labels dictionary including operation_type.
+        """
+        return super().get_core_labels(
+            metadata=metadata,
+            operation_type=operation_type,
+        )
+
+    def close(self) -> None:
+        """
+        Close the PI Web API client and shutdown monitoring services.
+
+        This method properly closes all connections and resources associated
+        with the PI Web API client and monitoring services.
+        """
+        self.pi_web_api_client.close()
+        SientiaMonitoring.shutdown(self)
+
+    async def process_pi_web_api_response(
+        self,
+        response_data: list[dict[str, Any]],
+        tags: dict[str, str],
+        core_labels: dict[str, str],
+        metadata: dict[str, Any],
+    ) -> tuple[int, str]:
+        """
+        Process the response data from PI Web API write operation.
+
+        Validates that all tags were successfully written, emits metrics for each tag
+        (success or error), and returns the appropriate prediction confidence value.
+        Sets error confidence if any tag write fails or if the number of written tags
+        doesn't match the expected count.
+
+        Args:
+            - response_data (dict[str, Any]): The response data from the PI Web API write operation.
+            - tags (dict[str, str]): The tags that were written to the PI Web API.
+            - core_labels (dict[str, str]): The core labels of the workflow execution.
+            - metadata (dict[str, Any]): The metadata of the workflow execution.
+        Returns:
+            int: Prediction confidence value (0 for success, 13 for errors)
+        """
+
+        # Convert tags from name:webid to webid:name
+        tags = {w: t for t, w in tags.items()}
+
+        tag_names = list[str](tags.values())
+
+        confidence = 0
+
+        message = ''
+
+        # Evaluate response for each tag
+        written_tags = []
+        for item in response_data:
+            web_id = item.get('WebId')
+            if not web_id:
+                self.error('The response did not contain some WebIds', metadata)
+                continue
+            errors = item.get('Errors', [])
+            tag_name = tags.get(web_id)
+            if not tag_name:
+                self.error(
+                    f'The response did not contain the tag name for WebId {web_id}', metadata
+                )
+                continue
+            if errors:
+                self.error(
+                    f'Error writing tag {tag_name}:{web_id} to PI Web API: {errors}', metadata
+                )
+                await self.emit_metric(
+                    metric_object=metrics.PI_WEB_API_PREDICTION_WRITTEN_ERROR_COUNT,
+                    tags={
+                        **core_labels,
+                        'tag_name': tag_name,
+                    },
+                )
+                confidence = PI_WEB_API_PREDICTION_ERROR_CONFIDENCE
+            else:
+                await self.emit_metric(
+                    metric_object=metrics.PI_WEB_API_PREDICTION_WRITTEN_COUNT,
+                    tags={
+                        **core_labels,
+                        'tag_name': tag_name,
+                    },
+                )
+                written_tags.append(tag_name)
+
+        if len(written_tags) != len(tag_names):
+            message = f'The number of written tags does not match the number of tag names: Expected {tag_names} tags, but {written_tags} tags were written.'
+
+            self.error(
+                f'{message}\nResponse:\n {json.dumps(response_data, indent=4)}\nTags:\n {json.dumps(tags, indent=4)}',
+                metadata,
+            )
+
+            await self.send_notification_async(
+                metadata=metadata,
+                notification_id='WRITE_PI_WEB_API_PREDICTION_ERROR',
+                message=f'The number of written tags does not match the number of tag names: Expected {tag_names} tags, but {written_tags} tags were written.\nResponse:\n {json.dumps(response_data, indent=4)}\nTags:\n {json.dumps(tags, indent=4)}',
+                block='write_pi_web_api_data',
+                level=NotificationLevel.ERROR,
+            )
+            confidence = PI_WEB_API_PREDICTION_ERROR_CONFIDENCE
+
+        return confidence, message
+
+    @activity.defn(name='write_pi_web_api_data')
+    async def write_pi_web_api_data(self, input_data: dict[str, Any]) -> dict[Any, Any]:
+        """
+        Write prediction and confidence data to PI Web API.
+
+        Writes prediction values and confidence scores to PI Web API using configured
+        web IDs. Processes responses to validate writes and emit metrics. Handles errors
+        gracefully by setting error confidence values when writes fail and sending
+        notifications for both prediction and confidence write errors.
+
+        Args:
+            input_data (dict[str, Any]): The input data containing:
+                - metadata (dict[str, Any]): Workflow execution metadata
+                - pi_web_api_output_config (dict[str, Any]): PI Web API configuration with:
+                    - endpoint (str): PI Web API endpoint URL
+                    - prediction_tags (dict[str, str]): Mapping of tag names to web IDs for predictions
+                    - confidence_tags (dict[str, str]): Mapping of tag names to web IDs for confidence
+                - data (dict[str, Any]): Prediction data, its a dataframe converted to dict.
+        Returns:
+            dict[Any, Any]: Data dictionary with potentially modified confidence values
+                If prediction write fails, prediction_confidence is set to error value (13)
+        """
+        metadata = input_data['metadata']
+        data = DataFrame(input_data['data'])
+        pi_web_api_output_config = input_data['pi_web_api_output_config']
+
+        self.info(f'Writing data to PI Web API... config: {pi_web_api_output_config}', metadata)
+
+        raw_prediction_tags = pi_web_api_output_config['prediction_tags']
+        raw_confidence_tags = pi_web_api_output_config['confidence_tags']
+        prediction_tags = list[str](raw_prediction_tags.values())
+        confidence_tags = list(raw_confidence_tags.values())
+
+        core_labels = self.get_pi_web_api_core_labels(metadata)
+
+        prediction_value = data.head(1)['prediction'].values[0]
+        confidence_value = data.head(1)['prediction_confidence'].values[0]
+
+        try:
+            prediction_response = await self.pi_web_api_client.write_value(
+                web_ids=prediction_tags,
+                value={
+                    'Timestamp': data.head(1)['timestamp'].values[0],
+                    'Value': prediction_value,
+                },
+                metadata=metadata,
+            )
+
+            confidence, message = await self.process_pi_web_api_response(
+                response_data=prediction_response,
+                tags=raw_prediction_tags,
+                core_labels=core_labels,
+                metadata=metadata,
+            )
+
+            # Preserve incoming confidence/comments on successful PI writes.
+            # Only downgrade confidence or override comments when PI response
+            # explicitly reports a problem (e.g. partial write mismatch).
+            if confidence != 0:
+                data['prediction_confidence'] = confidence
+            if message:
+                data['comments'] = message
+
+        except Exception as e:
+            trace = traceback.format_exc()
+            await self.send_notification_async(
+                metadata=metadata,
+                notification_id='WRITE_PI_WEB_API_PREDICTION_ERROR',
+                message=f'Error writing prediction data to PI Web API: {e}\n Tags: {raw_prediction_tags}',
+                block='write_pi_web_api_data',
+                level=NotificationLevel.ERROR,
+                attachment_content=trace,
+            )
+
+            self.error(trace, metadata)
+
+            data['prediction_confidence'] = PI_WEB_API_PREDICTION_ERROR_CONFIDENCE
+            data['comments'] = str(e)
+
+            return data.to_dict()
+
+        try:
+            confidence_response = await self.pi_web_api_client.write_value(
+                web_ids=confidence_tags,
+                value={
+                    'Timestamp': data.head(1)['timestamp'].values[0],
+                    'Value': float(confidence_value),
+                },
+                metadata=metadata,
+            )
+
+            await self.process_pi_web_api_response(
+                response_data=confidence_response,
+                tags=raw_confidence_tags,
+                core_labels=core_labels,
+                metadata=metadata,
+            )
+
+        except Exception as e:
+            trace = traceback.format_exc()
+            await self.send_notification_async(
+                metadata=metadata,
+                notification_id='WRITE_PI_WEB_API_CONFIDENCE_ERROR',
+                message=f'Error writing confidence data to PI Web API: {e}\n Tags: {raw_confidence_tags}',
+                block='write_pi_web_api_data',
+                level=NotificationLevel.ERROR,
+                attachment_content=trace,
+            )
+
+        return data.to_dict()

laborious/activities/gates.py

@@ -0,0 +1,804 @@
+from sientia_do.repository.minio_repository import MinioRepository
+from temporalio import activity, workflow
+
+from laborious.utils.repository.minio_manager import MinioManager
+
+with workflow.unsafe.imports_passed_through():
+    import traceback
+    from collections.abc import Callable, Mapping
+    from typing import Any
+
+    from pandas import DataFrame
+    from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
+    from sientia_do.notifications.models import NotificationLevel
+    from sientia_do.observability.logger import Logger
+    from sientia_do.observability.metrics_controller import MetricsController
+    from sientia_do.utils.formatters import create_sample_dict
+
+    from laborious import metrics
+    from laborious.utils.dataframe_debug import build_dataframe_debug_message
+    from laborious.utils.filters.conditional_filters import (
+        filter_empty_data,
+        filter_specific_variables_null_values,
+    )
+    from laborious.utils.filters.mlflow_filters import api_error_filter, nan_values_filter
+    from laborious.utils.models.minio_dataframe_payload import MinioDataFramePayload
+
+# Strongly-typed filter function signatures
+InputFilterFunc = Callable[[DataFrame, dict[str, Any]], bool]
+ResponseFilterFunc = Callable[[dict[str, Any], dict[str, Any]], bool]
+ContentFilterFunc = Callable[[DataFrame, dict[str, Any]], bool]
+
+# Input filter function mappings
+input_filter_functions: dict[str, InputFilterFunc] = {
+    'SPECIFIC_VARIABLES_NULL_VALUES': filter_specific_variables_null_values,
+    'EMPTY_DATA': filter_empty_data,
+}
+
+# Confidence mappings kept separate from function maps to avoid Union types
+input_path_confidence: Mapping[str, int] = {
+    'STOP': -1,
+    'CONTINUE': 2,
+    'REPEAT': -1,
+}
+
+# MLFlow response filter function mappings
+mlflow_response_filter_functions: dict[str, ResponseFilterFunc] = {
+    'API_ERROR': api_error_filter,
+}
+
+mlflow_response_path_confidence: Mapping[str, int] = {
+    'STOP': -1,
+    'CONTINUE': 10,
+    'REPEAT': -1,
+}
+
+# MLFlow content filter function mappings
+mlflow_content_filter_functions: dict[str, ContentFilterFunc] = {
+    'NAN_VALUES': nan_values_filter,
+    'EMPTY_DATA': filter_empty_data,
+}
+
+mlflow_content_path_confidence: Mapping[str, int] = {
+    'STOP': -1,
+    'CONTINUE': 18,
+    'REPEAT': -1,
+}
+
+
+class Gates(MinioManager):
+    """
+    Data quality gates and filtering activities for the Laborious system.
+
+    This class implements comprehensive data quality validation and filtering
+    mechanisms that can be applied at different stages of the prediction pipeline.
+    It provides configurable filters with policy-based decision making to ensure
+    data integrity and quality throughout the ML workflow.
+
+    The class supports multiple filter types and implements a flexible policy
+    system that can be configured for different validation requirements. Each
+    filter returns a path decision (STOP, CONTINUE, REPEAT) along with confidence
+    scores and detailed comments for monitoring and debugging.
+
+    Attributes:
+        input_filter_functions (dict): Mapping of input filter names to functions
+        mlflow_response_filter_functions (dict): Mapping of MLFlow response filter names to functions
+        mlflow_content_filter_functions (dict): Mapping of MLFlow content filter names to functions
+    """
+
+    minio_repository: MinioRepository | None = None
+    _MAX_DEBUG_DATAFRAME_ROWS = 100
+
+    def __init__(
+        self,
+        minio_repository: MinioRepository | None = None,
+        logger: Logger | None = None,
+        notification_handler: NotificationHandler | None = None,
+        metrics_controller: MetricsController | None = None,
+    ):
+        """
+        Initialize data quality gates with logging and notification capabilities.
+
+        Args:
+            logger: Logger instance for observability and debugging
+            notification_handler: Notification handler for alerts and monitoring
+
+        Raises:
+            Exception: If BaseActivity initialization fails
+        """
+        MinioManager.__init__(
+            self, minio_repository, logger, notification_handler, metrics_controller
+        )
+
+    def close(self) -> None:
+        """
+        Close the gates activity and clean up resources.
+        """
+
+        MinioManager.close(self)
+
+    def __del__(self):
+        self.close()
+
+    def _debug_dataframe(self, message: str, data: Any, metadata: dict[str, Any]) -> None:
+        """
+        Log dataframe content only when row count is below the configured threshold
+
+        Args:
+         - message (str): Base log message to identify the dataframe in logs
+         - data (Any): Dataframe-like payload to be logged
+         - metadata (dict[str, Any]): Workflow metadata for contextual logging
+        """
+        self.debug(
+            build_dataframe_debug_message(
+                message=message,
+                data=data,
+                max_rows=self._MAX_DEBUG_DATAFRAME_ROWS,
+            ),
+            metadata,
+        )
+
+    @staticmethod
+    def _read_filter_entry(config: dict[str, Any]) -> tuple[str, dict[str, Any]]:
+        """
+        Read filter policy/config keys in a case-insensitive way.
+
+        Args:
+            config (dict[str, Any]): Filter configuration dictionary.
+
+        Return:
+            tuple[str, dict[str, Any]]: Parsed policy and config payload.
+        """
+        normalized = {str(key).upper(): value for key, value in config.items()}
+        policy = normalized['POLICY']
+        filter_config = normalized.get('CONFIG', {})
+        return policy, filter_config
+
+    @activity.defn(name='input_gate')
+    async def input_gate(self, input_data: dict[str, Any]) -> tuple[str | None, int, str]:
+        """
+        Apply input data quality filters and validation.
+
+        This activity validates input data quality using configurable filters
+        before proceeding with ML operations. It applies multiple filter types
+        and returns a path decision based on the filter results and configured
+        policies.
+
+        The method implements a comprehensive filtering system that:
+        1. Applies configured filters to input data
+        2. Evaluates filter results against policy configurations
+        3. Determines appropriate path decisions (STOP, CONTINUE, REPEAT)
+        4. Provides confidence scores and detailed comments
+        5. Handles errors gracefully with notification integration
+
+        Args:
+            input_data: Configuration and data for input validation
+                Required keys:
+                    - metadata (dict): Workflow execution metadata
+                    - filters (dict): Filter configuration and policies
+                    - data (dict): Input data to validate
+                    - path_priority (list[str]): Priority order for path decisions
+
+        Returns:
+            tuple: (path_flag, confidence, comment)
+                - path_flag (str | None): Decision path (STOP, CONTINUE, REPEAT, or None)
+                - confidence (int): Confidence score for the decision
+                - comment (str): Detailed explanation of the decision
+
+        Raises:
+            Exception: If filter execution fails or configuration is invalid
+        """
+        metadata = input_data['metadata']
+
+        self.info('Performing input gate...', metadata)
+
+        filters = input_data['filters']
+        payload = MinioDataFramePayload.from_dict(input_data['data'])
+        data = await payload.retrieve(self.minio_repository, metadata)
+        path_priority = input_data['path_priority']
+
+        filter_output = []
+
+        self._debug_dataframe('Input data:', data, metadata)
+        self.debug(f'Filters: {filters}', metadata)
+
+        # Apply each configured filter
+        for fil, config in filters.items():
+            if fil not in input_filter_functions:
+                self.error(f'Filter {fil} not found', metadata)
+                continue
+            policy, filter_config = self._read_filter_entry(config)
+            try:
+                if input_filter_functions[fil](data, filter_config):
+                    self.debug(f'Data not passed the input filter {fil}:{config}', metadata)
+                    filter_output.append(policy)
+            except Exception as e:
+                trace = traceback.format_exc()
+                await self.send_notification_async(
+                    metadata=metadata,
+                    notification_id=f'INTPUT_GATE_ERROR__{fil}',
+                    message=f'Error in filter {fil}:{config}: \n {e}',
+                    block='input_gate',
+                    level=NotificationLevel.ERROR,
+                    attachment_content=trace,
+                )
+
+        for path_flag in path_priority:
+            if path_flag in filter_output:
+                self.info(f'Input gate result: {path_flag}', metadata)
+                return path_flag, input_path_confidence[path_flag], 'Input data with bad quality'
+
+        self.info('Nothing was filtered by the input gate', metadata)
+
+        del data
+
+        return None, 0, ''
+
+    @activity.defn(name='mlflow_response_gate')
+    async def mlflow_response_gate(self, input_data: dict[str, Any]) -> tuple[str | None, int, str]:
+        """
+        Validate MLFlow API response quality and integrity.
+
+        This activity validates MLFlow API responses to ensure they meet quality
+        standards before proceeding with further processing. It applies response-specific
+        filters and determines appropriate path decisions based on response quality.
+
+        The method implements response validation that:
+        1. Applies MLFlow response-specific filters
+        2. Evaluates API response quality and integrity
+        3. Determines path decisions based on response validation results
+        4. Provides confidence scores and detailed validation comments
+        5. Handles API errors and response validation failures
+
+        Args:
+            input_data: Configuration and data for response validation
+                Required keys:
+                    - metadata (dict): Workflow execution metadata
+                    - filters (dict): Response filter configuration and policies
+                    - data (dict): MLFlow API response data to validate
+                    - type (str): Type of MLFlow operation (transform, predict)
+                    - path_priority (list[str]): Priority order for path decisions
+
+        Returns:
+            tuple: (path_flag, confidence, comment)
+                - path_flag (str | None): Decision path (STOP, CONTINUE, REPEAT, or None)
+                - confidence (int): Confidence score for the decision
+                - comment (str): Detailed explanation of the decision
+
+        Raises:
+            Exception: If response validation fails or configuration is invalid
+        """
+        metadata = input_data['metadata']
+        self.info('Performing mlflow response gate...', metadata)
+        raw_data = input_data['data']
+        filters = input_data['filters']
+
+        self.debug(
+            f'Input data: \n {create_sample_dict(raw_data, max_items=5, max_depth=5)}', metadata
+        )
+        self.debug(f'Filters: {filters}', metadata)
+
+        payload = MinioDataFramePayload.from_dict(raw_data)
+        data = await payload.retrieve(self.minio_repository, metadata)
+
+        gate_type = input_data['type']
+        path_priority = input_data['path_priority']
+
+        filter_output = []
+
+        comments = []
+
+        status = payload.status or {}
+
+        for fil, config in filters.items():
+            if fil not in mlflow_response_filter_functions:
+                continue
+            policy, filter_config = self._read_filter_entry(config)
+            try:
+                if mlflow_response_filter_functions[fil](status, filter_config):
+                    filter_output.append(policy)
+                    comments.append(status.get('message', 'Unknown MLFlow API error'))
+                    await self.send_notification_async(
+                        metadata=metadata,
+                        notification_id=f'{gate_type.upper()}_GATE_RESPONSE_FILTER__{fil}',
+                        message=status.get('message', 'Unknown MLFlow API error'),
+                        block='mlflow_gate',
+                        level=NotificationLevel.ERROR,
+                        attachment_content=status.get('traceback'),
+                    )
+            except Exception as e:
+                trace = traceback.format_exc()
+                await self.send_notification_async(
+                    metadata=metadata,
+                    notification_id=f'MLFLOW_GATE_RESPONSE_FILTER__{fil}',
+                    message=f'Error in filter {fil}:{config}: \n {e}',
+                    block='mlflow_gate',
+                    level=NotificationLevel.ERROR,
+                    attachment_content=trace,
+                )
+
+        for path_flag in path_priority:
+            if path_flag in filter_output:
+                self.info(f'Mlflow response gate result: {path_flag}', metadata)
+                return path_flag, mlflow_response_path_confidence[path_flag], ', '.join(comments)
+
+        self.info('Nothing was filtered by the mlflow response gate', metadata)
+
+        del data
+
+        return None, 0, ''
+
+    @activity.defn(name='mlflow_content_gate')
+    async def mlflow_content_gate(self, input_data: dict[str, Any]) -> tuple[str | None, int, str]:
+        """
+        Validate MLFlow prediction content quality and integrity.
+
+        This activity validates the content of MLFlow predictions to ensure they
+        meet quality standards before export and persistence. It applies content-specific
+        filters and determines appropriate path decisions based on content quality.
+
+        The method implements content validation that:
+        1. Applies MLFlow content-specific filters
+        2. Evaluates prediction content quality and integrity
+        3. Determines path decisions based on content validation results
+        4. Provides confidence scores and detailed validation comments
+        5. Handles content validation failures and quality issues
+
+        Args:
+            input_data: Configuration and data for content validation
+                Required keys:
+                    - metadata (dict): Workflow execution metadata
+                    - filters (dict): Content filter configuration and policies
+                    - data (dict): MLFlow prediction content to validate
+                    - type (str): Type of MLFlow operation (transform, predict)
+                    - path_priority (list[str]): Priority order for path decisions
+
+        Returns:
+            tuple: (path_flag, confidence, comment)
+                - path_flag (str | None): Decision path (STOP, CONTINUE, REPEAT, or None)
+                - confidence (int): Confidence score for the decision
+                - comment (str): Detailed explanation of the decision
+
+        Raises:
+            Exception: If content validation fails or configuration is invalid
+        """
+        metadata = input_data['metadata']
+        self.info('Performing mlflow content gate...', metadata)
+
+        filters = input_data['filters']
+
+        payload = MinioDataFramePayload.from_dict(input_data['data'])
+        data = await payload.retrieve(self.minio_repository, metadata)
+
+        gate_type = input_data['type']
+        path_priority = input_data['path_priority']
+
+        filter_output = []
+
+        self._debug_dataframe('Input data:', data, metadata)
+        self.debug(f'Filters: \n {filters}', metadata)
+
+        for fil, config in filters.items():
+            if fil not in mlflow_content_filter_functions:
+                continue
+            policy, filter_config = self._read_filter_entry(config)
+            try:
+                if mlflow_content_filter_functions[fil](data, filter_config):
+                    filter_output.append(policy)
+                    await self.send_notification_async(
+                        metadata=metadata,
+                        notification_id=f'{gate_type.upper()}_GATE_CONTENT_FILTER__{fil}',
+                        message=f'Data not passed the content filter {fil}:{config}',
+                        block='mlflow_gate',
+                        level=NotificationLevel.WARNING,
+                        attachment_content=data.to_string(),
+                    )
+            except Exception as e:
+                trace = traceback.format_exc()
+                await self.send_notification_async(
+                    metadata=metadata,
+                    notification_id=f'MLFLOW_GATE_CONTENT_FILTER__{fil}',
+                    message=f'Error in filter {fil}:{config}: \n {e}',
+                    block='mlflow_gate',
+                    level=NotificationLevel.ERROR,
+                    attachment_content=trace,
+                )
+
+        for path_flag in path_priority:
+            if path_flag in filter_output:
+                self.info(f'Mlflow content gate result: {path_flag}', metadata)
+                return (
+                    path_flag,
+                    mlflow_content_path_confidence[path_flag],
+                    'Transformed data not passed the content filter',
+                )
+
+        self.info('Nothing was filtered by the mlflow content gate', metadata)
+
+        del data
+
+        return None, 0, ''
+
+    def get_prediction_store_policy(
+        self, prediction_store_policy: str, metadata: dict[str, Any]
+    ) -> tuple[str, int]:
+        """
+        Parse and validate prediction store policy configuration.
+
+        This method parses prediction store policy strings in the format 'type:value'
+        and validates them against allowed policy types and values. It provides
+        sensible defaults for invalid configurations and logs policy validation
+        failures for operational monitoring.
+
+        Supported Policy Types:
+        - 'lts': Latest timestamp - sorts data by timestamp descending
+        - 'erl': Earliest timestamp - sorts data by timestamp ascending
+
+        Args:
+            prediction_store_policy (str): Policy string in format 'type:value'
+            metadata (dict[str, Any]): Context metadata for logging and notifications
+
+        Returns:
+            tuple[str, int]: (policy_type, policy_value)
+                - policy_type (str): Validated policy type ('lts' or 'erl')
+                - policy_value (int): Number of rows to retain
+        """
+        policy_elements = prediction_store_policy.split(':')
+
+        if len(policy_elements) < 2:
+            self.error(
+                f'Invalid prediction store policy: {prediction_store_policy}, using default policy',
+                metadata,
+            )
+            return 'lts', 1
+
+        policy_type = policy_elements[0]
+        policy_value = policy_elements[1]
+
+        # If the policy_type is not lts or erl, we use the default policy
+        # If the policty_value is not a number or 0, we use the default policy
+        if (
+            policy_type not in ['lts', 'erl']
+            or not policy_value.isdigit()
+            or int(policy_value) == 0
+        ):
+            self.error(
+                f'Invalid prediction store policy: {prediction_store_policy}, using default policy',
+                metadata,
+            )
+            return 'lts', 1
+
+        return policy_type, int(policy_value)
+
+    @activity.defn(name='format_transformed_data')
+    async def format_transformed_data(self, input_data: dict[str, Any]) -> MinioDataFramePayload:
+        """
+        Format transformed data for storage and export operations.
+
+        This method formats transformed data from MLFlow model transformations
+        into a standardized format suitable for database storage. It converts
+        wide-format data (columns as variables) into long-format (melted)
+        with proper timestamp handling and model identification.
+
+        The formatting process includes:
+        1. Converting input data dictionary to DataFrame
+        2. Extracting timestamps from DataFrame index
+        3. Resetting index to create sequential row numbers
+        4. Melting data from wide format to long format (variable-value pairs)
+        5. Adding model_id for data lineage tracking
+
+        Args:
+            input_data (dict): Input data containing:
+                - metadata (dict): Workflow execution metadata
+                - data (dict[str, Any]): Transformed data to format (DataFrame-compatible dict)
+                - model_id (str): Unique identifier for the ML model
+
+        Returns:
+            dict: Formatted data dictionary with keys:
+                - timestamp (dict): Timestamp values indexed by row number
+                - variable (dict): Variable names indexed by row number
+                - value (dict): Variable values indexed by row number
+                - model_id (dict): Model identifiers indexed by row number
+        """
+        metadata = input_data['metadata']
+
+        model_id = input_data['model_id']
+
+        self.info('Formatting transformed data...', metadata)
+
+        payload = MinioDataFramePayload.from_dict(input_data['data'])
+        data = await payload.retrieve(self.minio_repository, metadata)
+
+        data['timestamp'] = data.index
+        data = data.reset_index(drop=True)
+
+        data = data.melt(id_vars='timestamp', var_name='variable', value_name='value')
+        data['model_id'] = model_id
+
+        return await MinioDataFramePayload.from_dataframe(
+            dataframe=data,
+            minio_repo=self.minio_repository,
+            model_name=input_data['model_name'],
+            operation='transform',
+            workflow_metadata=metadata,
+            last_timestamp=payload.last_timestamp,
+            logger=self.logger,
+        )
+
+    @activity.defn(name='format_prediction')
+    async def format_prediction(self, input_data: dict[str, Any]) -> dict:
+        """
+        Format prediction data according to configured storage policies.
+
+        This method formats prediction data for storage and export operations.
+        It applies timestamp-based sorting policies, adds metadata fields,
+        and ensures data consistency before persistence. The method supports
+        multiple storage policies for flexible data retention strategies.
+
+        If only one row is present, we use the last timestamp as the timestamp
+
+        Storage Policies:
+        - 'lts:N': Latest timestamp - retains N most recent predictions
+        - 'erl:N': Earliest timestamp - retains N oldest predictions
+
+        Args:
+            input_data (dict): Input data containing:
+                - data (dict[str, Any]): Raw prediction data to format
+                - timestamp (str): Timestamp of the data
+                - model_id (str): Unique identifier for the ML model
+                - prediction_confidence (float): Confidence score for the prediction
+                - prediction_store_policy (str): Storage policy in format 'type:value'
+
+        Returns:
+            dict: Formatted prediction data ready for storage and export
+        """
+        metadata = input_data['metadata']
+        last_timestamp = input_data['timestamp']
+        prediction_store_policy = input_data['prediction_store_policy']
+        self.info('Formatting prediction...', metadata)
+
+        payload = MinioDataFramePayload.from_dict(input_data['data'])
+        data = await payload.retrieve(self.minio_repository, metadata)
+
+        # Create timestamp column from index and reset index
+        data['timestamp'] = data.index
+        data = data.reset_index(drop=True)
+
+        self.debug(f'Prediction store policy: {prediction_store_policy}', metadata)
+        self._debug_dataframe('Prediction data:', data, metadata)
+
+        policy_type, policy_value = self.get_prediction_store_policy(
+            prediction_store_policy, metadata
+        )
+
+        # If data has no timestamp, we use the default timestamp and not sort the data
+        self.info(
+            f'Sorting data by timestamp and applying policy: {policy_type}:{policy_value}', metadata
+        )
+
+        # If policy_type is lts, we need to sort the data by timestamp descending and take the first policy_value rows
+        if policy_type == 'lts':
+            self.debug('Sorting data by timestamp descending', metadata)
+            data = data.sort_values(by='timestamp', ascending=False)
+        # If policy_type is erl, we need to sort the data by timestamp ascending and take the first policy_value rows
+        elif policy_type == 'erl':
+            self.debug('Sorting data by timestamp ascending', metadata)
+            data = data.sort_values(by='timestamp', ascending=True)
+        else:
+            self.error(f'Invalid policy type: {policy_type}, using default policy', metadata)
+            raise ValueError(f'Invalid policy type: {policy_type}')
+
+        int_policy_value = int(policy_value)
+
+        data = data.head(int_policy_value)
+        if int_policy_value == 1:
+            data['timestamp'] = last_timestamp
+
+        data['model_id'] = input_data['model_id']
+        data['prediction_confidence'] = input_data['prediction_confidence']
+        data['prediction_status'] = 'Good'
+        data['comments'] = ''
+        data = data.sort_values(by='timestamp', ascending=False)
+        data = data.reset_index(drop=True)
+
+        self.info(f'Prediction formatted: {len(data)} rows', metadata)
+        self._debug_dataframe('Prediction data:', data, metadata)
+
+        return data.to_dict()
+
+    @activity.defn(name='format_default_prediction')
+    async def format_default_prediction(self, input_data: dict[str, Any]) -> dict:
+        """
+        Create and format default prediction data for error conditions.
+
+        This method generates default prediction data when the main prediction
+        pipeline encounters errors or quality issues. It creates a standardized
+        data structure with zero values for predictions and useful metadata
+        for operational monitoring and debugging.
+
+        The default prediction serves as a fallback mechanism to:
+        1. Maintain data pipeline continuity during failures
+        2. Provide operational visibility into prediction quality issues
+        3. Enable downstream systems to handle error conditions gracefully
+        4. Support debugging and troubleshooting efforts
+
+        Args:
+            input_data (dict): Input data containing:
+                - timestamp (str): Timestamp for the default prediction
+                - model_id (str): Unique identifier for the ML model
+                - prediction_confidence (float): Confidence score (typically low for errors)
+                - comment (str): Error description or operational comment
+
+        Returns:
+            dict: Formatted default prediction data with error indicators
+        """
+
+        metadata = input_data['metadata']
+        self.debug('Formatting default prediction...', metadata)
+
+        data = DataFrame(
+            {
+                'prediction': [0],
+                'response_time': [0],
+                'timestamp': [input_data['timestamp']],
+                'model_id': [input_data['model_id']],
+                'prediction_confidence': [input_data['prediction_confidence']],
+                'prediction_status': ['Bad'],
+                'comments': [input_data['comment']],
+            }
+        )
+
+        self.info(f'Default prediction formatted: {data.size} rows', metadata)
+        return data.to_dict()
+
+    @activity.defn(name='format_retrain_report')
+    async def format_retrain_report(self, input_data: dict[str, Any]) -> dict:
+        """
+        Format retrain report data for storage and audit trail maintenance.
+
+        This method formats model retraining operation results into a standardized
+        report format suitable for database storage and operational monitoring.
+        It captures retraining status, timestamps, and model version information
+        for comprehensive audit trails and operational visibility.
+
+        The formatting process includes:
+        1. Extracting retraining experiment response data
+        2. Capturing model update report information (version, MLflow IDs)
+        3. Formatting timestamps and status information
+        4. Conditionally including version information for successful retrains
+
+        Args:
+            input_data (dict): Input data containing:
+                - metadata (dict): Workflow execution metadata
+                - experiment_response (dict): Retraining experiment response containing:
+                    - success (bool): Retraining operation success status
+                    - timestamp (str): Timestamp of the retraining operation
+                    - message (str): Status message or error description
+                - update_report (dict): Model update report containing:
+                    - version (str): New model version identifier
+                    - mlflow_run_id (str): MLflow run identifier
+                    - mlflow_experiment_id (str): MLflow experiment identifier
+                - model_id (str): Unique identifier for the ML model
+                - model_name (str): Name of the ML model
+
+        Returns:
+            dict: Formatted retrain report dictionary with keys:
+                - model_id (dict): Model identifiers indexed by row number
+                - model_name (dict): Model names indexed by row number
+                - timestamp (dict): Retraining timestamps indexed by row number
+                - status (dict): Retraining status messages indexed by row number
+                - version (dict, optional): Model versions indexed by row number
+                    Only included if experiment_response['success'] is True
+                - mlflow_run_id (dict, optional): MLflow run IDs indexed by row number
+                    Only included if experiment_response['success'] is True
+                - mlflow_experiment_id (dict, optional): MLflow experiment IDs indexed by row number
+                    Only included if experiment_response['success'] is True
+        """
+        metadata = input_data['metadata']
+        self.info('Formatting retrain report...', metadata)
+
+        experiment_response = input_data['experiment_response']
+        update_report = input_data['update_report']
+        model_id = input_data['model_id']
+        model_name = input_data['model_name']
+
+        report = DataFrame(
+            {
+                'model_id': [model_id],
+                'model_name': [model_name],
+                'timestamp': [experiment_response['timestamp']],
+                'status': [experiment_response['message']],
+            }
+        )
+
+        if experiment_response['success']:
+            # Retrain was successfull
+            report['version'] = update_report['version']
+            report['mlflow_run_id'] = update_report['mlflow_run_id']
+            report['mlflow_experiment_id'] = update_report['mlflow_experiment_id']
+
+        self._debug_dataframe('Retrain report:', report, metadata)
+
+        return report.to_dict()
+
+    @activity.defn(name='write_metrics')
+    async def write_metrics(self, input_data: dict[str, Any]):
+        """
+        Write prediction performance metrics to Prometheus monitoring system.
+
+        This method records comprehensive metrics for prediction operations,
+        enabling operational monitoring, performance analysis, and alerting.
+        It tracks prediction counts, confidence levels, and response times
+        for each model and pipeline combination.
+
+        Metrics Recorded:
+        1. Prediction Count: Incremental counter for successful predictions
+        2. Confidence Monitor: Current confidence level for predictions
+        3. Response Time Monitor: Histogram of prediction response times
+
+        Args:
+            input_data (dict): Input data containing:
+                - metadata (dict[str, Any]): Workflow execution metadata
+                - prediction (dict[str, Any]): Prediction data with metrics
+
+        Raises:
+            Exception: If metrics writing fails or configuration is invalid
+        """
+        metadata = input_data['metadata']
+        prediction = DataFrame(input_data['prediction'])
+        prediction_confidence = prediction['prediction_confidence'].values[0]
+        response_time = prediction['response_time'].values[0]
+        opc_metrics = input_data['opc_metrics']
+
+        self.info(f'Writing metrics for model {metadata["model_name"]}', metadata)
+
+        core_tags = {
+            'pod_id': self.pod_id,
+            'runtime': self.runtime,
+            'operation_type': 'predict',
+            'model_name': metadata['model_name'],
+            'workflow_name': metadata['workflow_name'],
+        }
+        await self.emit_metric(
+            metric_object=metrics.PREDICTIONS_WRITTEN_COUNT,
+            tags=core_tags,
+        )
+
+        await self.emit_metric(
+            metric_object=metrics.PREDICTION_CONFIDENCE_MONITOR,
+            method='set',
+            tags=core_tags,
+            value=prediction_confidence,
+        )
+
+        await self.emit_metric(
+            metric_object=metrics.PREDICTION_RESPONSE_TIME_MONITOR,
+            method='observe',
+            tags=core_tags,
+            value=response_time,
+        )
+
+        for server_id, tags in opc_metrics.items():
+            for tag, response_time in tags.items():
+                if response_time is not None:
+                    await self.emit_metric(
+                        metric_object=metrics.PREDICTION_OPC_WRITING_RESPONSE_TIME_MONITOR,
+                        method='observe',
+                        tags={
+                            **core_tags,
+                            'opc_server_id': server_id,
+                            'tag': tag,
+                        },
+                        value=response_time,
+                    )
+
+                    await self.emit_metric(
+                        metric_object=metrics.PREDICTION_OPC_WRITING_COUNT,
+                        tags={
+                            **core_tags,
+                            'opc_server_id': server_id,
+                            'tag': tag,
+                        },
+                    )
+
+        self.info(f'Metrics written for model {metadata["model_name"]}', metadata)

laborious/activities/mlflow.py

@@ -0,0 +1,528 @@
+from temporalio import activity, workflow
+
+with workflow.unsafe.imports_passed_through():
+    import traceback
+    from typing import Any
+
+    import numpy as np
+    from pandas import to_datetime
+    from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
+    from sientia_do.notifications.models import NotificationLevel
+    from sientia_do.observability.logger import Logger
+    from sientia_do.observability.metrics_controller import MetricsController
+    from sientia_do.repository.minio_repository import MinioRepository
+    from sientia_do.temporal.constants import (
+        DATETIME_FORMAT,
+        DATETIME_FORMAT_MS_WITH_TZ,
+        DATETIME_FORMAT_WITH_TZ,
+        now,
+    )
+    from sientia_do.utils.formatters import create_sample_dict
+
+    from laborious.utils.dataframe_debug import build_dataframe_debug_message
+    from laborious.utils.models.minio_dataframe_payload import MinioDataFramePayload
+    from laborious.utils.repository.minio_manager import MinioManager
+    from laborious.utils.repository.model_repository import MLFlowRepository
+
+
+class MLFlow(MinioManager):
+    """
+    MLFlow integration activities for model inference operations.
+
+    This class provides activities for interacting with MLFlow models, including
+    data transformation and prediction operations. It handles authentication,
+    data preprocessing, and model management with configurable retention policies.
+
+    The class implements comprehensive error handling and logging for all
+    MLFlow operations, ensuring reliable model inference in production environments.
+
+    Attributes:
+        mlflow_host (str): MLFlow server hostname
+        mlflow_port (int): MLFlow server port
+        mlflow_username (str): MLFlow authentication username
+        mlflow_password (str): MLFlow authentication password
+        model_monitoring_repository (MLFlowRepository): Repository for MLFlow operations
+    """
+
+    _MAX_DEBUG_DATAFRAME_ROWS = 100
+
+    def __init__(
+        self,
+        mlflow_host: str,
+        mlflow_port: int,
+        mlflow_username: str,
+        mlflow_password: str,
+        minio_repository: MinioRepository | None = None,
+        logger: Logger | None = None,
+        notification_handler: NotificationHandler | None = None,
+        metrics_controller: MetricsController | None = None,
+    ):
+        """
+        Initialize MLFlow activities with server configuration.
+
+        Args:
+            mlflow_host: MLFlow server hostname or IP address
+            mlflow_port: MLFlow server port number
+            mlflow_username: Username for MLFlow authentication
+            mlflow_password: Password for MLFlow authentication
+            logger: Logger instance for observability and debugging
+            notification_handler: Notification handler for alerts and monitoring
+
+        Raises:
+            Exception: If MLFlowRepository initialization fails
+        """
+        MinioManager.__init__(
+            self, minio_repository, logger, notification_handler, metrics_controller
+        )
+        self.mlflow_host = mlflow_host
+        self.mlflow_port = mlflow_port
+        self.mlflow_username = mlflow_username
+        self.mlflow_password = mlflow_password
+
+        self.model_monitoring_repository = MLFlowRepository(
+            f'{mlflow_host}:{mlflow_port}',
+            mlflow_username,
+            mlflow_password,
+            logger,
+            notification_handler,
+            metrics_controller,
+        )
+
+    def close(self) -> None:
+        """
+        Close the MLFlow activity and clean up resources.
+        """
+        MinioManager.close(self)
+
+    def __del__(self):
+        self.close()
+
+    def _debug_dataframe(self, message: str, data: Any, metadata: dict[str, Any]) -> None:
+        """
+        Log dataframe content only when row count is below the configured threshold
+
+        Args:
+         - message (str): Base log message to identify the dataframe in logs
+         - data (Any): Dataframe-like object expected to expose shape and to_csv
+         - metadata (dict[str, Any]): Workflow metadata for contextual logging
+        """
+        self.debug(
+            build_dataframe_debug_message(
+                message=message,
+                data=data,
+                max_rows=self._MAX_DEBUG_DATAFRAME_ROWS,
+            ),
+            metadata,
+        )
+
+    @activity.defn(name='request_transform')
+    async def request_transform(self, input_data: dict[str, Any]) -> MinioDataFramePayload:
+        """
+        Transform input data using MLFlow models.
+
+        This activity processes input data through MLFlow model transformation,
+        including data preprocessing, format conversion, and validation. It handles
+        data deduplication, pivoting, and cleanup to ensure optimal model performance.
+
+        The transformation process includes:
+        1. Data deduplication based on variable and timestamp
+        2. Data pivoting for model input format
+        3. Null value handling and cleanup
+        4. MLFlow model transformation request
+        5. Response validation and logging
+
+        Args:
+            input_data: Configuration and data for transformation
+                Required keys:
+                    - metadata (dict): Workflow execution metadata
+                    - data (dict): Input data for transformation
+                    - model_name (str): Name of the MLFlow model to use
+                    - model_retention (int): Model retention period in minutes
+
+        Returns:
+            dict: Transformed data from MLFlow model
+
+        Raises:
+            Exception: If transformation fails or MLFlow model is unavailable
+        """
+        metadata = input_data['metadata']
+        self.info('Transforming data...', metadata)
+
+        payload = MinioDataFramePayload.from_dict(input_data['data'])
+        data = await payload.retrieve(self.minio_repository, metadata)
+
+        model_name = input_data['model_name']
+        model_config = input_data.get('model_config', {})
+
+        self._debug_dataframe('Raw input data:', data, metadata)
+
+        # Sort by created_at in descending order and keep first occurrence of each variable/timestamp pair
+        data = data.sort_values('created_at', ascending=False).drop_duplicates(
+            subset=['variable', 'timestamp'], keep='first'
+        )
+
+        # Pivot data for model input format
+        data = data.pivot(index='timestamp', columns='variable', values='value')
+        data.fillna(np.nan, inplace=True)
+
+        data.columns.name = None
+        data.index.name = None
+
+        data['timestamp'] = data.index
+
+        self._debug_dataframe('Processed input data:', data, metadata)
+
+        # Request transformation from MLFlow model
+        response_data = await self.model_monitoring_repository.transform(
+            model_name, data, model_config, metadata
+        )
+
+        self.debug(
+            f'Transform raw response data: \n {create_sample_dict(response_data, max_items=5, max_depth=5)}',
+            metadata,
+        )
+
+        self.debug(
+            f'Transform response data: \n {create_sample_dict(response_data, max_items=5, max_depth=5)}',
+            metadata,
+        )
+
+        self.info('Data transformed successfully', metadata)
+
+        if not response_data.get('success', False):
+            return await MinioDataFramePayload.from_dataframe(
+                dataframe=None,
+                minio_repo=self.minio_repository,
+                model_name=model_name,
+                operation='transform',
+                status=response_data,
+                workflow_metadata=metadata,
+                last_timestamp=payload.last_timestamp,
+                logger=self.logger,
+            )
+
+        return await MinioDataFramePayload.from_dataframe(
+            dataframe=response_data['content'],
+            minio_repo=self.minio_repository,
+            model_name=model_name,
+            operation='transform',
+            workflow_metadata=metadata,
+            status={
+                'success': True,
+            },
+            last_timestamp=payload.last_timestamp,
+            logger=self.logger,
+        )
+
+    @activity.defn(name='request_predict')
+    async def request_predict(self, input_data: dict[str, Any]) -> MinioDataFramePayload:
+        """
+        Execute predictions using MLFlow models.
+
+        This activity performs ML model inference using MLFlow models with the
+        transformed data. It handles data format conversion, null value processing,
+        and model prediction requests with comprehensive error handling.
+
+        The prediction process includes:
+        1. Data format validation and cleanup
+        2. Null value handling for model compatibility
+        3. MLFlow model prediction request
+        4. Response validation and logging
+        5. Performance monitoring and metrics
+
+        Args:
+            input_data: Configuration and data for prediction
+                Required keys:
+                    - metadata (dict): Workflow execution metadata
+                    - data (dict): Transformed data for prediction
+                    - model_name (str): Name of the MLFlow model to use
+                    - model_retention (int): Model retention period in minutes
+
+        Returns:
+            dict: Prediction results from MLFlow model
+
+        Raises:
+            Exception: If prediction fails or MLFlow model is unavailable
+        """
+        metadata = input_data['metadata']
+        self.info('Predicting data...', metadata)
+
+        payload = MinioDataFramePayload.from_dict(input_data['data'])
+        data = await payload.retrieve(self.minio_repository, metadata)
+
+        model_name = input_data['model_name']
+        model_config = input_data.get('model_config', {})
+
+        self._debug_dataframe('Input data for prediction:', data, metadata)
+
+        # Convert numpy.nan to None for model compatibility
+        data.replace(np.nan, None, inplace=True)
+
+        data['timestamp'] = data.index
+        data['timestamp'] = to_datetime(
+            data['timestamp'], format=DATETIME_FORMAT_WITH_TZ
+        ).dt.strftime(DATETIME_FORMAT)
+
+        # Request prediction from MLFlow model
+        response_data = await self.model_monitoring_repository.predict(
+            model_name, data, model_config, metadata
+        )
+
+        self.debug(
+            f'Prediction response data: \n {create_sample_dict(response_data, max_items=5, max_depth=5)}',
+            metadata,
+        )
+
+        self.info('Data predicted successfully', metadata)
+
+        if not response_data.get('success', False):
+            return await MinioDataFramePayload.from_dataframe(
+                dataframe=None,
+                minio_repo=self.minio_repository,
+                model_name=model_name,
+                operation='predict',
+                status=response_data,
+                workflow_metadata=metadata,
+                last_timestamp=payload.last_timestamp,
+                logger=self.logger,
+            )
+
+        return await MinioDataFramePayload.from_dataframe(
+            dataframe=response_data['content'],
+            minio_repo=self.minio_repository,
+            model_name=model_name,
+            operation='predict',
+            workflow_metadata=metadata,
+            status={
+                'success': True,
+            },
+            last_timestamp=payload.last_timestamp,
+            logger=self.logger,
+        )
+
+    @activity.defn(name='retrain_model')
+    async def retrain_model(self, input_data: dict[str, Any]) -> dict[str, Any]:
+        """
+        Retrain MLFlow models with updated training data.
+
+        This activity orchestrates the complete model retraining process,
+        including data preparation, model retraining execution, and result
+        validation. It handles data preprocessing, column cleanup, and
+        comprehensive error handling for production model management.
+
+        The retraining process includes:
+        1. Data timestamp extraction and validation
+        2. Column cleanup and data preparation
+        3. Data pivoting for model input format
+        4. MLFlow model retraining execution
+        5. Result validation and error handling
+
+        Args:
+            input_data (dict): Input data containing:
+                - metadata (dict): Workflow execution metadata
+                - data (dict[str, Any]): Training data for model retraining
+                - model_name (str): Name of the MLFlow model to retrain
+
+        Returns:
+            dict: Retraining results containing:
+                - status (str): Retraining operation status
+                - timestamp (str): Timestamp of the retraining operation
+                - experiment (str): MLFlow experiment identifier
+
+        Raises:
+            Exception: If retraining fails or encounters critical errors
+        """
+
+        if self.minio_repository is None:
+            raise ValueError('Minio repository not initialized')
+
+        metadata = input_data['metadata']
+
+        try:
+            # Payload-based retrain input (inline dict or MinIO offloaded).
+            payload = MinioDataFramePayload.from_dict(input_data['data'])
+            data = await payload.retrieve(self.minio_repository, metadata)
+
+        except Exception as e:
+            trace = traceback.format_exc()
+            await self.send_notification_async(
+                metadata=metadata,
+                notification_id='ERROR_LOADING_RETRAIN_DATA',
+                message=f'Error loading retrain data: {e}',
+                block='retrain_model',
+                level=NotificationLevel.ERROR,
+                attachment_content=trace,
+            )
+            self.error(trace, metadata)
+            return {
+                'success': False,
+                'message': f'Error loading retrain data: {e}',
+                'traceback': trace,
+                'timestamp': now().strftime(DATETIME_FORMAT_MS_WITH_TZ),
+            }
+
+        self.debug(f'Retrain data loaded successfully: shape {data.shape}', metadata)
+
+        model_name = input_data['model_name']
+        model_config = input_data.get('model_config', {})
+
+        self.info(f'Retraining model {model_name}...', metadata)
+
+        timestamp = data['timestamp'].max()
+        self.debug(f'Timestamp: {timestamp}', metadata)
+
+        # Sort by created_at in descending order and keep first occurrence of each variable/timestamp pair
+        if 'created_at' in data.columns:
+            data = data.sort_values('created_at', ascending=False).drop_duplicates(
+                subset=['variable', 'timestamp'], keep='first'
+            )
+        else:
+            data = data.drop_duplicates(subset=['variable', 'timestamp'], keep='first')
+
+        data.drop(columns=['model_id'], inplace=True, errors='ignore')
+        data.drop(columns=['created_at'], inplace=True, errors='ignore')
+
+        # Pivot data for model input format
+        data = data.pivot(index='timestamp', columns='variable', values='value')
+        data.fillna(np.nan, inplace=True)
+        # data.reset_index(inplace=True)
+        data.columns.name = None
+
+        data['timestamp'] = data.index
+        data['timestamp'] = to_datetime(
+            data['timestamp'], format=DATETIME_FORMAT_WITH_TZ
+        ).dt.strftime(DATETIME_FORMAT)
+        data['timestamp'] = to_datetime(data['timestamp'], format=DATETIME_FORMAT)
+
+        data.columns.name = None
+
+        retrain_output = await self.model_monitoring_repository.retrain_model(
+            data=data, model_name=model_name, model_config=model_config, metadata=metadata
+        )
+
+        if not retrain_output['success']:
+            trace = retrain_output['traceback']
+            await self.send_notification_async(
+                metadata=metadata,
+                notification_id='RETRAIN_MODEL_ERROR',
+                message=f'Error retraining model {model_name}: {retrain_output["message"]}',
+                block='retrain_model',
+                level=NotificationLevel.ERROR,
+                attachment_content=trace,
+            )
+            self.error(trace, metadata=metadata)
+
+        return {**retrain_output, 'timestamp': timestamp}
+
+    @activity.defn(name='update_production_model')
+    async def update_production_model(self, input_data: dict[str, Any]) -> dict[Any, Any]:
+        """
+        Update production model with newly trained model version.
+
+        This activity manages the critical process of updating production
+        models with newly trained versions. It handles model deployment,
+        status tracking, and comprehensive reporting for operational
+        visibility and audit trails.
+
+        The update process includes:
+        1. Production model update execution
+        2. Status and metadata tracking
+        3. Comprehensive reporting and logging
+        4. Error handling and notification
+        5. Audit trail maintenance
+
+        Args:
+            input_data (dict): Input data containing:
+                - metadata (dict): Workflow execution metadata
+                - model_name (str): Name of the MLFlow model to update
+                - experiment (str): MLFlow experiment identifier
+                - model_id (str): Unique identifier for the model version
+                - timestamp (str): Timestamp of the update operation
+                - status (str): Current status of the model update
+
+        Returns:
+            dict[Any, Any]: Comprehensive update report containing:
+                - model_id (str): Model version identifier
+                - model_name (str): Name of the updated model
+                - timestamp (str): Update operation timestamp
+                - status (str): Update operation status
+                - Additional MLFlow response metadata
+
+        Raises:
+            Exception: If production model update fails
+        """
+        metadata = input_data['metadata']
+        model_name = input_data['model_name']
+        experiment = input_data['experiment']
+        self.info(
+            f'Updating production model {model_name} from experiment {experiment}...', metadata
+        )
+
+        try:
+            response = await self.model_monitoring_repository.update_production_model(
+                experiment=experiment, model_name=model_name, metadata=metadata
+            )
+
+            self.info(f'Production model {model_name} updated successfully', metadata)
+            return response
+
+        except Exception as e:
+            trace = traceback.format_exc()
+            await self.send_notification_async(
+                metadata=metadata,
+                notification_id='UPDATE_PRODUCTION_MODEL_ERROR',
+                message=f'Error updating production model {model_name}: {e}',
+                block='update_production_model',
+                level=NotificationLevel.ERROR,
+                attachment_content=trace,
+            )
+            self.error(trace, metadata=metadata)
+            raise e
+
+    @activity.defn(name='get_reference_data')
+    async def get_reference_data(self, input_data: dict[str, Any]) -> list[dict] | None:
+        """
+        Get reference data from the MLflow Model Registry.
+
+        This method retrieves evaluation reference data stored as artifacts in the
+        MLflow Model Registry. The reference data is typically used for model
+        drift detection, performance comparison, and quality validation. The method
+        loads the data from a CSV artifact file and formats timestamps for
+        consistent processing.
+
+        The method handles:
+        1. Loading evaluation data artifact from MLflow Model Registry
+        2. Timestamp parsing and formatting for consistency
+        3. Data conversion to dictionary format for workflow consumption
+        4. Graceful handling of missing reference data
+
+        Args:
+            input_data (dict): Input data containing:
+                - metadata (dict): Workflow execution metadata
+                - model_name (str): Name of the MLFlow model to get reference data from
+
+        Returns:
+            list[dict[Hashable, Any]] | None: Reference data from the MLflow Model Registry
+                as a list of dictionaries. Returns None if reference data is not found
+                or if the artifact does not exist.
+
+        Raises:
+            Exception: If artifact loading fails or encounters errors during processing
+        """
+
+        metadata = input_data['metadata']
+        model_name = input_data['model_name']
+        artifact = 'evaluation_data.csv'
+
+        reference_data = await self.model_monitoring_repository.load_artifact_dataframe(
+            model_name=model_name, artifact_path=artifact, metadata=metadata
+        )
+
+        if reference_data is None:
+            self.warning(f'Reference data not found for model {model_name}', metadata)
+            return None
+
+        reference_data['timestamp'] = to_datetime(reference_data['timestamp'])
+        reference_data['timestamp'] = reference_data['timestamp'].dt.strftime(DATETIME_FORMAT)
+
+        return reference_data.to_dict(orient='records')

laborious/activities/model_metrics.py

@@ -0,0 +1,364 @@
+from temporalio import activity, workflow
+
+with workflow.unsafe.imports_passed_through():
+    import time
+    import traceback
+    import warnings
+    from typing import Any
+
+    import numpy as np
+    from pandas import DataFrame, Index, to_datetime
+    from sientia.ModelAnalysis import ModelAnalysis
+    from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
+    from sientia_do.notifications.models import NotificationLevel
+    from sientia_do.observability.logger import Logger
+    from sientia_do.observability.metrics_controller import MetricsController
+    from sientia_do.observability.sientia_monitoring import SientiaMonitoring
+    from sientia_do.temporal.constants import DATETIME_FORMAT, DATETIME_FORMAT_WITH_TZ
+
+    from laborious import metrics
+    from laborious.utils.dataframe_debug import build_dataframe_debug_message
+
+warnings.filterwarnings('ignore', category=RuntimeWarning, message='Degrees of freedom <= 0')
+warnings.filterwarnings(
+    'ignore', category=RuntimeWarning, message='invalid value encountered in scalar divide'
+)
+
+
+class ModelMetrics(SientiaMonitoring):
+    """
+    Metrics activities for the Laborious system.
+
+    This class provides activities for writing metrics to the Prometheus monitoring system.
+    """
+
+    _MAX_DEBUG_DATAFRAME_ROWS = 100
+
+    def __init__(
+        self,
+        logger: Logger,
+        notification_handler: NotificationHandler,
+        metrics_controller: MetricsController,
+    ):
+        SientiaMonitoring.__init__(self, logger, notification_handler, metrics_controller)
+
+    def close(self) -> None:
+        """
+        Close the model metrics activity and clean up resources.
+        """
+        SientiaMonitoring.shutdown(self)
+
+    def __del__(self):
+        self.close()
+
+    def _debug_dataframe(self, message: str, data: Any, metadata: dict[str, Any]) -> None:
+        """
+        Log dataframe content only when row count is below the configured threshold
+
+        Args:
+         - message (str): Base log message to identify the dataframe in logs
+         - data (Any): Dataframe-like payload to be logged
+         - metadata (dict[str, Any]): Workflow metadata for contextual logging
+        """
+        self.debug(
+            build_dataframe_debug_message(
+                message=message,
+                data=data,
+                max_rows=self._MAX_DEBUG_DATAFRAME_ROWS,
+            ),
+            metadata,
+        )
+
+    async def get_drift_metrics(
+        self,
+        reference_data: DataFrame,
+        target_data: DataFrame,
+        target_name: str,
+        reference_columns: Index,
+        drift_metrics: list[str],
+        chunk_period: str,
+        metadata: dict[str, Any],
+    ) -> DataFrame:
+        """
+        Calculate univariate drift metrics for a model.
+        Args:
+            model_analysis (ModelAnalysis): Model analysis object
+            reference_data (DataFrame): Reference data
+            target_data (DataFrame): Target data
+            reference_columns (list[str]): Reference columns
+            drift_metrics (list[str]): Drift metrics
+            metadata (dict[str, Any]): Workflow execution metadata
+        """
+
+        config = {
+            'target': target_name,
+            'prediction': 'prediction',
+            'timestamp': 'timestamp',
+            'features': reference_columns,
+        }
+
+        model_analysis = ModelAnalysis(config=config)
+
+        self._debug_dataframe(
+            f'Reference data: Size {reference_data.shape}', reference_data, metadata
+        )
+
+        self._debug_dataframe(f'Target data: Size {target_data.shape}', target_data, metadata)
+
+        core_labels = self.get_core_labels(metadata, operation_type='detect_univariate_drift')
+        start_time = time.time()
+        try:
+            univariate_drift = model_analysis.detect_univariate_drift(
+                reference_df=reference_data,
+                analysis_df=target_data,
+                features=reference_columns,
+                timestamp_col=config['timestamp'],
+                methods=drift_metrics,
+                chunk_period=chunk_period,
+            )
+        except Exception as e:
+            self.error(f'Error detecting univariate drift: {e}', metadata)
+            await self.emit_metric(
+                metric_object=metrics.MODEL_ANALYZE_ERROR_COUNT, tags=core_labels
+            )
+            raise e
+        await self.observe_lag(start_time, metrics.MODEL_ANALYZE_LAG, core_labels)
+        await self.emit_metric(metric_object=metrics.MODEL_ANALYZE_COUNT, tags=core_labels)
+
+        core_labels = self.get_core_labels(metadata, operation_type='detect_multivariate_drift')
+        start_time = time.time()
+        try:
+            multivariate_drift = model_analysis.detect_multivariate_drift(
+                reference_df=reference_data,
+                analysis_df=target_data,
+                features=reference_columns,
+                timestamp_col=config['timestamp'],
+                chunk_period=chunk_period,
+            )
+        except Exception as e:
+            self.error(f'Error detecting multivariate drift: {e}', metadata)
+            await self.emit_metric(
+                metric_object=metrics.MODEL_ANALYZE_ERROR_COUNT, tags=core_labels
+            )
+            raise e
+        await self.observe_lag(start_time, metrics.MODEL_ANALYZE_LAG, core_labels)
+        await self.emit_metric(metric_object=metrics.MODEL_ANALYZE_COUNT, tags=core_labels)
+
+        start_time = time.time()
+        core_labels = self.get_core_labels(metadata, operation_type='get_drift_metrics_dataframe')
+        try:
+            drift_df = model_analysis.get_drift_metrics_dataframe(
+                univariate_drift=univariate_drift,
+                multivariate_drift=multivariate_drift,
+            )
+        except Exception as e:
+            self.error(f'Error getting drift metrics: {e}', metadata)
+            await self.emit_metric(
+                metric_object=metrics.MODEL_ANALYZE_ERROR_COUNT, tags=core_labels
+            )
+            raise e
+        await self.observe_lag(start_time, metrics.MODEL_ANALYZE_LAG, core_labels)
+        await self.emit_metric(metric_object=metrics.MODEL_ANALYZE_COUNT, tags=core_labels)
+
+        self._debug_dataframe(f'Drift dataframe: Size {drift_df.shape}', drift_df, metadata)
+
+        return drift_df
+
+    @activity.defn(name='calculate_drift')
+    async def calculate_drift(self, input_data: dict[str, Any]) -> list[dict]:
+        """
+        Calculate drift metrics for a model.
+
+        Args:
+            input_data (dict[str, Any]): Input data containing:
+                - metadata (dict): Workflow execution metadata
+                - model_name (str): Name of the MLFlow model to calculate drift for
+                - reference_data (pd.DataFrame): Reference data for the model
+                - target_data (pd.DataFrame): Target data for calculating drift
+                - target_name (str): Name of the target column
+                - drift_metrics (list[str]): List of drift metrics to calculate
+        """
+        metadata = input_data['metadata']
+        model_name = input_data['model_name']
+        model_id = input_data['model_id']
+        reference_raw_data = input_data['reference_data']
+        target_data = DataFrame(input_data['target_data'])
+        target_name = input_data['target_name']
+        drift_metrics = input_data['drift_metrics']
+        chunk_period = input_data['chunk_period']
+
+        if chunk_period not in ['min', 's']:
+            self.error(f'Invalid chunk period: {chunk_period}', metadata)
+            raise ValueError(f'Invalid chunk period: {chunk_period}, must be "min" or "s"')
+
+        self.info(f'Calculating drift for model {model_name}', metadata)
+
+        target_data = target_data.pivot(index='timestamp', columns='variable', values='value')
+        target_data['timestamp'] = target_data.index
+        target_data['timestamp'] = to_datetime(target_data['timestamp'])
+        target_data['timestamp'] = target_data['timestamp'].dt.strftime(DATETIME_FORMAT)
+        target_data = target_data.reset_index(drop=True)
+        target_data.dropna(inplace=True)
+
+        if reference_raw_data is not None:
+            self.info('Using reference data', metadata)
+            reference_data = DataFrame(reference_raw_data)
+            accurate = True
+        else:
+            # Get 30% first rows of target_data
+            self.warning('Using 30% first rows of target data as reference data', metadata)
+            target_data.sort_values(by='timestamp', ascending=True, inplace=True)
+            reference_data = target_data.head(int(len(target_data) * 0.3))
+            accurate = False
+
+            await self.send_notification_async(
+                metadata=metadata,
+                notification_id='MODEL_METRICS_REFERENCE_DATA_WARNING',
+                message='Using 30% first rows of target data as reference data',
+                block='model_metrics',
+                level=NotificationLevel.WARNING,
+                attachment_content=reference_data.to_csv(),
+            )
+
+        reference_columns = reference_data.drop(
+            columns=[target_name, 'timestamp', 'target', 'prediction'], errors='ignore'
+        ).columns
+
+        try:
+            drift_df = await self.get_drift_metrics(
+                reference_data=reference_data,
+                target_data=target_data,
+                target_name=target_name,
+                reference_columns=reference_columns,
+                drift_metrics=drift_metrics,
+                chunk_period=chunk_period,
+                metadata=metadata,
+            )
+        except Exception as e:
+            self.error(f'Error getting drift metrics: {e}', metadata)
+            await self.send_notification_async(
+                metadata=metadata,
+                notification_id='MODEL_METRICS_GET_DRIFT_METRICS_ERROR',
+                message=f'Error getting drift metrics: {e}',
+                block='model_metrics',
+                level=NotificationLevel.ERROR,
+                attachment_content=traceback.format_exc(),
+            )
+            return []
+
+        if drift_df.empty:
+            self.warning('No drift metrics found', metadata)
+            return []
+
+        # Drop unnecessary columns
+        drift_df.drop(columns=['p_value'], inplace=True)
+
+        # Extract timestamps only until minutes
+        if chunk_period == 'min':
+            target_timestamps = target_data['timestamp'].apply(lambda x: x[:16])
+        else:
+            target_timestamps = target_data['timestamp']
+
+        # Drop rows where timestamp is not in target data, to avoid save drift from reference
+        drift_df = drift_df[drift_df['timestamp'].isin(target_timestamps)]
+
+        if drift_df.empty:
+            self.warning(
+                'No drift metrics found after dropping rows where timestamp is not in target data',
+                metadata,
+            )
+            return []
+
+        # Rename columns to match database columns
+        drift_df.rename(
+            columns={
+                'metric': 'method',
+                'statistic': 'value',
+            },
+            inplace=True,
+        )
+
+        # Drop duplicates
+        drift_df.drop_duplicates(
+            subset=['timestamp', 'method', 'feature'], keep='first', inplace=True
+        )
+
+        drift_df['model_id'] = model_id
+        drift_df['accurate'] = accurate
+
+        drift_df['timestamp'] = to_datetime(drift_df['timestamp'])
+        drift_df['timestamp'] = drift_df['timestamp'].dt.tz_localize('UTC')
+        drift_df['timestamp'] = drift_df['timestamp'].dt.strftime(DATETIME_FORMAT_WITH_TZ)
+
+        self._debug_dataframe(f'Drift dataframe: Size {drift_df.shape}', drift_df, metadata)
+
+        return drift_df.to_dict(orient='records')
+
+    @activity.defn(name='calculate_simple_metrics')
+    async def calculate_simple_metrics(self, input_data: dict[str, Any]) -> list[dict]:
+        """
+        Calculate simple metrics for a model. Metrics available are:
+            - rmse
+            - mse
+            - mae
+            - r2
+            - accuracy
+            - precision
+            - recall
+            - f1
+        Args:
+            input_data (dict[str, Any]): Input data containing:
+                - metadata (dict): Workflow execution metadata
+                - model_id (str): ID of the MLFlow model
+                - target_data (pd.DataFrame): Target data for calculating metrics, containing target and prediction columns
+                - metrics (list[str]): List of metrics to calculate
+        Returns:
+            dict[Hashable, Any]: Dictionary containing the calculated metrics
+        """
+
+        metadata = input_data['metadata']
+        model_id = input_data['model_id']
+        target_data = DataFrame(input_data['target_data'])
+        metrics = input_data['metrics']
+        interval_minutes = input_data['interval_minutes']
+
+        data_size = target_data.shape[0]
+
+        output_data = []
+
+        diff = target_data['target'] - target_data['prediction']
+        diff_squared = diff**2
+
+        self.info(f'Calculating simple metrics for model {model_id}: {metrics}', metadata)
+
+        for metric in metrics:
+            if metric == 'rmse':
+                output_data.append({'metric': 'rmse', 'value': np.sqrt(np.mean(diff_squared))})
+            elif metric == 'mse':
+                output_data.append({'metric': 'mse', 'value': np.mean(diff_squared)})
+            elif metric == 'mae':
+                output_data.append({'metric': 'mae', 'value': np.mean(np.abs(diff))})
+            elif metric == 'r2':
+                y_true = target_data['target']
+                y_mean = np.mean(y_true)
+
+                ss_res = np.sum(diff_squared)
+                ss_tot = np.sum((y_true - y_mean) ** 2)
+
+                # Evita divisão por zero
+                if ss_tot == 0:
+                    r2_score = 0.0
+                else:
+                    r2_score = 1 - (ss_res / ss_tot)
+
+                output_data.append({'metric': 'r2', 'value': r2_score})
+
+        data = DataFrame(output_data)
+        data['model_id'] = model_id
+        data['timestamp'] = target_data['timestamp'].max()
+        data['data_size'] = data_size
+        data['interval_minutes'] = interval_minutes
+
+        self._debug_dataframe(f'Simple metrics dataframe: Size {data.shape}', data, metadata)
+
+        return data.to_dict(orient='records')

laborious/activities/opc.py

@@ -0,0 +1,515 @@
+from temporalio import activity, workflow
+
+with workflow.unsafe.imports_passed_through():
+    import traceback
+    from collections.abc import Hashable
+    from typing import Any
+
+    from pandas import DataFrame
+    from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
+    from sientia_do.notifications.models import NotificationLevel
+    from sientia_do.observability.logger import Logger
+    from sientia_do.observability.metrics_controller import MetricsController
+    from sientia_do.observability.sientia_monitoring import SientiaMonitoring
+
+    from laborious.utils.repository.opc_repository import OpcRepository
+
+OPC_WRITTING_ERROR_CONFIDENCE = 12
+OPC_SESSION_BAD_CONFIDENCE = 14
+OPC_SESSION_BAD_COMMENT_PREFIX = 'OPC UA session/channel error:'
+OPC_WRITTING_ERROR_MESSAGE = 'Some data could not be written to OPC servers'
+OPC_RECONNECT_IN_PROGRESS_COMMENT = 'OPC UA reconnect in progress'
+OPC_COMMENT_SEPARATOR = ' | '
+
+
+def _opc_session_bad_comment(opc_status: str | None) -> str:
+    status = opc_status or 'Unknown'
+    return f'{OPC_SESSION_BAD_COMMENT_PREFIX} {status}'
+
+
+def _apply_opc_write_error(
+    error_info: dict[str, Any] | None,
+    session_bad_seen: bool,
+    session_bad_status: str | None,
+    reconnect_in_progress_seen: bool,
+) -> tuple[bool, str | None, bool]:
+    """
+    Update session/reconnect flags from an OPC write error payload.
+
+    Args:
+        error_info: Repository error details, or None when the write succeeded.
+        session_bad_seen: Whether a session_bad error was seen so far.
+        session_bad_status: Last known OPC status for session errors.
+        reconnect_in_progress_seen: Whether reconnect_in_progress was seen so far.
+
+    Return:
+        Updated (session_bad_seen, session_bad_status, reconnect_in_progress_seen).
+    """
+    if not error_info:
+        return session_bad_seen, session_bad_status, reconnect_in_progress_seen
+
+    kind = error_info.get('opc_error_kind')
+    if kind == 'session_bad':
+        return True, error_info.get('opc_status', session_bad_status), reconnect_in_progress_seen
+    if kind == 'reconnect_in_progress':
+        return session_bad_seen, session_bad_status, True
+    return session_bad_seen, session_bad_status, reconnect_in_progress_seen
+
+
+class OPC(SientiaMonitoring):
+    """
+    OPC server integration activities for real-time data export.
+
+    This class provides comprehensive OPC UA client functionality for connecting
+    to multiple OPC servers and writing prediction data in real-time. It implements
+    secure communication with certificate-based authentication and automatic
+    reconnection capabilities.
+
+    The class supports multiple OPC servers with individual configurations and
+    provides robust error handling and monitoring for production environments.
+
+    Attributes:
+        opc_servers (dict): Configuration for multiple OPC servers
+        opc_repository (dict): Active OPC repository connections
+        logger (Logger): Logging and observability instance
+        notification_handler (NotificationHandler): Notification management instance
+    """
+
+    def __init__(
+        self,
+        opc_servers: dict[str, dict[str, Any]],
+        logger: Logger,
+        notification_handler: NotificationHandler,
+        metrics_controller: MetricsController,
+    ):
+        self.logger = logger
+        self.notification_handler = notification_handler
+        self.opc_servers = opc_servers
+
+        SientiaMonitoring.__init__(self, logger, notification_handler, metrics_controller)
+
+        self.opc_repository: dict[str, OpcRepository] = {}
+
+    async def init_opc(self):
+        """
+        Initialize OPC server connections and establish communication channels.
+
+        This method iterates through all configured OPC servers and attempts to
+        establish secure connections using certificate-based authentication.
+        Each server connection is managed independently, and connection failures
+        are reported through the notification system.
+
+        The method performs the following operations:
+        1. Creates OpcRepository instances for each configured server
+        2. Establishes secure connections with certificate validation
+        3. Reports connection success/failure through notifications
+        4. Logs connection status for operational visibility
+
+        Raises:
+            Exception: If OPC repository initialization fails or connection
+                establishment encounters critical errors
+
+        Note:
+            Connection failures are logged and reported but do not prevent
+            the initialization of other OPC servers. Each server is handled
+            independently to ensure maximum availability.
+        """
+        self.logger.info('Initializing OPC servers...')
+        for opc_id, server in self.opc_servers.items():
+            self.opc_repository[opc_id] = OpcRepository(
+                opc_id=server['id'],
+                server_name=server['server_name'],
+                url=server['url'],
+                logger=self.logger,
+                server_uri=server['server_uri'],
+                cert_path=server['cert_path'],
+                private_key_path=server['private_key_path'],
+                server_cert_path=server['server_cert_path'],
+                notification_handler=self.notification_handler,
+                reconnection_interval=server['reconnection_interval'],
+                metrics_controller=self.metrics_controller,
+            )
+            is_connected, error_data = await self.opc_repository[opc_id].connect()
+            if not is_connected:
+                await self.send_notification_async(
+                    metadata={
+                        'model_id': '-',
+                        'model_name': '-',
+                        'workflow_name': '-',
+                        'schedule_name': 'INITIALIZATION',
+                    },
+                    notification_id=error_data['notification_id'],
+                    message=error_data['message'],
+                    block=error_data['block'],
+                    level=error_data.get('level', NotificationLevel.ERROR),
+                    attachment_content=error_data.get('attachment_content', None),
+                )
+            else:
+                self.logger.info(
+                    f'OPC server {opc_id}:{server["server_name"]} connected successfully.'
+                )
+
+    async def write_data(
+        self,
+        server_id: str,
+        tag: str,
+        data: Any,
+        data_type: str,
+        tag_type: str,
+        metadata: dict[str, Any],
+    ) -> tuple[float | None, dict[str, Any] | None]:
+        """
+        Write data to a specific OPC server tag with comprehensive error handling.
+
+        Return:
+            tuple[float | None, dict[str, Any] | None]: Response time on success, or
+                (None, error info_data) on repository failure.
+        """
+
+        try:
+            is_success, info_data = await self.opc_repository[server_id].write_data(
+                tag, data, data_type, metadata
+            )
+            if not is_success:
+                await self.send_notification_async(
+                    metadata=metadata,
+                    notification_id=info_data['notification_id'],
+                    message=info_data['message'],
+                    block=info_data['block'],
+                    level=info_data.get('level', NotificationLevel.ERROR),
+                    attachment_content=info_data.get('attachment_content', None),
+                )
+                return None, info_data
+            return info_data['response_time'], None
+        except Exception as e:
+            trace = traceback.format_exc()
+            await self.send_notification_async(
+                metadata=metadata,
+                notification_id=f'WRITE_OPC_{tag_type.upper()}_ERROR',
+                message=f'Error writing data to OPC server: {e}',
+                block='write_opc_data',
+                level=NotificationLevel.ERROR,
+                attachment_content=trace,
+            )
+            raise e
+
+    async def validate_server(self, server_id: str, metadata: dict[str, Any]) -> bool:
+        """
+        Validate that an OPC server is available and configured for write operations.
+
+        This method checks if the specified OPC server exists in the active
+        repository and is available for data writing operations. It provides
+        immediate feedback for server availability and logs validation failures
+        for operational monitoring.
+
+        Args:
+            server_id (str): Unique identifier for the OPC server to validate
+            metadata (dict[str, Any]): Context metadata for logging and notifications
+
+        Returns:
+            bool: True if server is available, False otherwise
+
+        Note:
+            Server validation failures are automatically reported through the
+            notification system with detailed information about available servers.
+            This helps operators quickly identify configuration issues.
+        """
+        if self.opc_repository.get(server_id) is None:
+            message = f'OPC server {server_id} not found to perform write operation.'
+            await self.send_notification_async(
+                metadata=metadata,
+                notification_id='OPC_SERVER_NOT_FOUND',
+                message=message,
+                block='write_opc_data',
+                level=NotificationLevel.ERROR,
+                attachment_content=f'OPC servers: {list(self.opc_repository.keys())}',
+            )
+            return False
+        return True
+
+    async def _write_tags_from_config(
+        self,
+        server_id: str,
+        tags_config: dict[str, dict[str, Any]],
+        data: DataFrame,
+        data_column: str,
+        tag_type: str,
+        log_label: str,
+        metadata: dict[str, Any],
+    ) -> tuple[dict[str, float | None], bool, str | None, bool]:
+        """
+        Write a group of OPC tags and collect response times and error flags.
+
+        Args:
+            server_id: Target OPC server identifier.
+            tags_config: Tag name to configuration mapping.
+            data: DataFrame with prediction/confidence columns.
+            data_column: Column name whose first row value is written.
+            tag_type: Tag category passed to write_data ('prediction' or 'confidence').
+            log_label: Human-readable label for success logs.
+            metadata: Context metadata for logging and notifications.
+
+        Return:
+            (response_times, session_bad_seen, session_bad_status, reconnect_in_progress_seen)
+        """
+        response_times: dict[str, float | None] = {}
+        session_bad_seen = False
+        session_bad_status: str | None = None
+        reconnect_in_progress_seen = False
+
+        for tag, tag_config in tags_config.items():
+            response_time, error_info = await self.write_data(
+                server_id=server_id,
+                tag=tag,
+                data=data.head(1)[data_column].values[0],
+                data_type=tag_config['data_type'],
+                tag_type=tag_type,
+                metadata=metadata,
+            )
+            session_bad_seen, session_bad_status, reconnect_in_progress_seen = (
+                _apply_opc_write_error(
+                    error_info,
+                    session_bad_seen,
+                    session_bad_status,
+                    reconnect_in_progress_seen,
+                )
+            )
+            if response_time is not None:
+                self.info(
+                    f'{log_label} written to OPC server {server_id} for tag {tag}.',
+                    metadata,
+                )
+            response_times[tag] = response_time
+
+        return response_times, session_bad_seen, session_bad_status, reconnect_in_progress_seen
+
+    async def manage_output_tags(
+        self,
+        server_id: str,
+        config: dict[str, Any],
+        data: DataFrame,
+        metadata: dict[str, Any],
+    ) -> tuple[bool, dict[str, float | None], bool, str | None, bool]:
+        """
+        Manage the writing of prediction and confidence data to OPC server tags.
+
+        This method orchestrates the writing of multiple data types to OPC servers
+        based on configuration. It handles both prediction data and confidence
+        values independently, allowing for flexible tag configuration and
+        comprehensive error handling.
+
+        The method supports two main tag types:
+        1. Prediction tags: Write actual prediction values to configured OPC tags
+        2. Confidence tags: Write confidence scores to separate OPC tags
+
+        Args:
+            server_id (str): Unique identifier for the target OPC server
+            config (dict[str, Any]): OPC tag configuration containing:
+                - prediction_tags (dict, optional): Prediction tag configurations
+                - confidence_tags (dict, optional): Confidence tag configurations
+            data (DataFrame): DataFrame containing prediction and confidence data
+            metadata (dict[str, Any]): Context metadata for logging and notifications
+            success (bool): Current success status to maintain across operations
+
+        Returns:
+            tuple[bool, int]: (overall_success, total_tags_written)
+                - overall_success: True if all configured tags were written successfully
+                - total_tags_written: Count of successfully written tags
+        """
+        response_times: dict[str, float | None] = {}
+        session_bad_seen = False
+        session_bad_status: str | None = None
+        reconnect_in_progress_seen = False
+
+        tag_groups = (
+            ('prediction_tags', 'prediction', 'prediction', 'Prediction data'),
+            ('confidence_tags', 'prediction_confidence', 'confidence', 'Confidence data'),
+        )
+        for config_key, data_column, tag_type, log_label in tag_groups:
+            if config_key not in config:
+                continue
+            (
+                group_times,
+                group_session_bad,
+                group_status,
+                group_reconnect,
+            ) = await self._write_tags_from_config(
+                server_id=server_id,
+                tags_config=config[config_key],
+                data=data,
+                data_column=data_column,
+                tag_type=tag_type,
+                log_label=log_label,
+                metadata=metadata,
+            )
+            response_times.update(group_times)
+            if group_session_bad:
+                session_bad_seen = True
+                session_bad_status = group_status or session_bad_status
+            if group_reconnect:
+                reconnect_in_progress_seen = True
+
+        success = None not in response_times.values()
+        return (
+            success,
+            response_times,
+            session_bad_seen,
+            session_bad_status,
+            reconnect_in_progress_seen,
+        )
+
+    @activity.defn(name='write_opc_data')
+    async def write_opc_data(
+        self, input_data: dict[str, Any]
+    ) -> tuple[dict[Hashable, Any], dict[str, dict[str, float | None]]]:
+        """
+        Write prediction and confidence data to OPC servers. The two writing
+        operations are optional and independent of each other.
+
+        Args:
+            - input_data(dict[str, Any]): The input data. Contains the following keys:
+                - data(dict[str, Any]): The dataframe that contains the data to write
+                to the OPC servers.
+                - opc_output_config(dict[str, Any]): The OPC writing configuration.
+                The keys are the OPC server names and the values contain:
+                    - prediction_tags(dict[str, Any]): The tags to write to the OPC servers.
+                    - confidence_tags(dict[str, Any]): The tags to write to the OPC servers.
+
+        Returns:
+            - dict[Any, Any]: The data that was written to the OPC servers.
+
+        """
+        metadata = input_data['metadata']
+        self.info('Writing data to OPC servers...', metadata)
+        data = DataFrame(input_data['data'])
+        opc_output_config = input_data['opc_output_config']
+        self.info(f'Data to write: {data.size} rows', metadata)
+
+        success = True
+        session_bad_seen = False
+        session_bad_status: str | None = None
+        reconnect_in_progress_seen = False
+
+        metrics: dict[str, dict[str, float | None]] = {}
+
+        for server_id, config in opc_output_config.items():
+            if not await self.validate_server(server_id, metadata):
+                success = False
+                continue
+
+            (
+                local_success,
+                local_response_times,
+                local_session_bad,
+                local_status,
+                local_reconnect_in_progress,
+            ) = await self.manage_output_tags(server_id, config, data, metadata)
+            metrics[server_id] = local_response_times
+            local_count = len(local_response_times)
+            success = success and local_success
+            if local_session_bad:
+                session_bad_seen = True
+                session_bad_status = local_status or session_bad_status
+            if local_reconnect_in_progress:
+                reconnect_in_progress_seen = True
+
+            self.info(
+                f'Process completed for OPC server {server_id}: {local_count} of {len(config.get("prediction_tags", []))} prediction tags and {len(config.get("confidence_tags", []))} confidence tags',
+                metadata,
+            )
+
+        return (
+            self.process_confidence(
+                data,
+                success,
+                metadata,
+                session_bad=session_bad_seen,
+                opc_status=session_bad_status,
+                reconnect_in_progress=reconnect_in_progress_seen,
+            ),
+            metrics,
+        )
+
+    def process_confidence(
+        self,
+        data: DataFrame,
+        success: bool,
+        metadata: dict[str, Any],
+        *,
+        session_bad: bool = False,
+        opc_status: str | None = None,
+        reconnect_in_progress: bool = False,
+    ) -> dict[Hashable, Any]:
+        """
+        Process prediction confidence based on OPC write operation success.
+
+        This method updates the prediction confidence values in the DataFrame
+        based on the success status of OPC server write operations. If any
+        write operations failed, it sets the confidence to a predefined error
+        value to indicate data quality issues.
+
+        The method implements a confidence degradation strategy:
+        - Success: Maintains original confidence values
+        - Failure: Sets confidence to error value for operational awareness
+
+        Args:
+            data (DataFrame): DataFrame containing prediction and confidence data
+            success (bool): Overall success status of OPC write operations
+            metadata (dict[str, Any]): Context metadata for logging and notifications
+
+        Returns:
+            dict[Any, Any]: Processed data as a dictionary with updated confidence values
+
+        Note:
+            The error confidence value (OPC_WRITTING_ERROR_CONFIDENCE = 12) is
+            used to indicate that data was not successfully exported to OPC servers.
+            This allows downstream systems to handle data quality appropriately.
+        """
+
+        if not success:
+            comment_parts: list[str] = []
+            confidence = OPC_WRITTING_ERROR_CONFIDENCE
+
+            if session_bad:
+                comment_parts.append(_opc_session_bad_comment(opc_status))
+                confidence = OPC_SESSION_BAD_CONFIDENCE
+            if reconnect_in_progress:
+                comment_parts.append(OPC_RECONNECT_IN_PROGRESS_COMMENT)
+                confidence = OPC_SESSION_BAD_CONFIDENCE
+            if not comment_parts:
+                comment_parts.append(OPC_WRITTING_ERROR_MESSAGE)
+
+            comments = OPC_COMMENT_SEPARATOR.join(comment_parts)
+            data['prediction_confidence'] = confidence
+            data['comments'] = comments
+            self.debug(
+                f'OPC write issues, confidence={confidence}, comments={comments}',
+                metadata,
+            )
+        else:
+            self.debug('Data written to OPC servers successfully.', metadata)
+
+        return data.to_dict()
+
+    async def close(self):
+        """
+        Gracefully shutdown all OPC server connections and cleanup resources.
+
+        This method ensures proper cleanup of all active OPC server connections
+        by calling the disconnect method on each repository instance. It's
+        designed to be called during application shutdown to prevent resource
+        leaks and ensure clean termination.
+
+        The method performs the following cleanup operations:
+        1. Iterates through all active OPC repository connections
+        2. Calls disconnect() on each repository instance
+        3. Allows for graceful connection termination
+        4. Prevents resource leaks and connection hanging
+
+        Note:
+            This method should be called during application shutdown to ensure
+            proper cleanup. It handles all active connections regardless of
+            their current state and provides a clean shutdown experience.
+        """
+        for opc in self.opc_repository.values():
+            await opc.disconnect()

laborious/activities/storage.py

@@ -0,0 +1,209 @@
+from temporalio import activity, workflow
+
+from laborious.utils.repository.minio_manager import MinioManager
+
+with workflow.unsafe.imports_passed_through():
+    # Extend the Temporal Postgres activities for convenient query -> MinIO export
+    import traceback
+    from datetime import timedelta
+    from typing import Any
+
+    import pandas as pd
+    from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
+    from sientia_do.notifications.models import NotificationLevel
+    from sientia_do.observability.logger import Logger
+    from sientia_do.observability.metrics_controller import MetricsController
+    from sientia_do.repository.minio_repository import MinioRepository
+    from sientia_do.temporal.activities.postgres import Postgres
+    from sientia_do.temporal.constants import now
+
+    from laborious.utils.models.minio_dataframe_payload import MinioDataFramePayload
+
+_LOAD_QUERY_OFFLOAD_SKIP_KEYS = frozenset({'model_name', 'key_prefix', 'size_threshold_bytes'})
+
+
+class Storage(Postgres, MinioManager):
+    """
+    Extensions for Postgres activities with a helper to export query results
+    directly to MinIO as Parquet and return the object name.
+    """
+
+    minio_repository: MinioRepository | None = None
+
+    def __init__(
+        self,
+        host: str,
+        port: int,
+        user: str,
+        password: str,
+        dbname: str,
+        min_connections: int,
+        max_connections: int,
+        retention_hours: int = 24,
+        minio_repository: MinioRepository | None = None,
+        logger: Logger | None = None,
+        notification_handler: NotificationHandler | None = None,
+        metrics_controller: MetricsController | None = None,
+    ):
+        self.retention_hours = retention_hours
+        Postgres.__init__(
+            self,
+            host=host,
+            port=port,
+            user=user,
+            password=password,
+            dbname=dbname,
+            min_connections=min_connections,
+            max_connections=max_connections,
+            logger=logger,
+            notification_handler=notification_handler,
+            metrics_controller=metrics_controller,
+        )
+
+        MinioManager.__init__(
+            self, minio_repository, logger, notification_handler, metrics_controller
+        )
+
+    @activity.defn(name='load_query_with_minio_offload')
+    async def load_query_with_minio_offload(
+        self, input_data: dict[str, Any]
+    ) -> MinioDataFramePayload:
+        """
+        Run the custom SQL load, then return a MinIO-aware dataframe wire dict.
+
+        Args (input_data):
+            metadata (dict): Workflow metadata (same as load_custom_query).
+            query (str): SQL query.
+            datetime_columns (list[str], optional): Datetime column names.
+            model_name (str): Model name for object key basename.
+            key_prefix (str, optional): Directory prefix inside the bucket.
+            size_threshold_bytes (int, optional): Override env offload threshold.
+
+        Returns:
+            dict[str, Any]: Flat ``MinioDataFramePayload`` dict or ``success: False`` on failure.
+        """
+        if self.minio_repository is None:
+            raise ValueError('Minio repository not initialized')
+
+        metadata: dict = input_data.get('metadata', {})
+        model_name = input_data['model_name']
+
+        rows = await self.load_custom_query(
+            input_data,
+        )
+        if not rows:
+            self.error(
+                'load_query_with_minio_offload failed: No data returned from query', metadata
+            )
+            dataframe = None
+        else:
+            dataframe = pd.DataFrame(rows)
+
+        return await MinioDataFramePayload.from_dataframe(
+            dataframe,
+            minio_repo=self.minio_repository,
+            workflow_metadata=metadata,
+            model_name=model_name,
+            operation='initial',
+            logger=self.logger,
+        )
+
+    @activity.defn(name='export_payload_to_postgres')
+    async def export_payload_to_postgres(self, input_data: dict[str, Any]) -> dict:
+        """
+        Export a payload to PostgreSQL.
+        """
+        metadata = input_data.get('metadata')
+        payload = MinioDataFramePayload.from_dict(input_data['data'])
+        data = await payload.retrieve(self.minio_repository, metadata)
+
+        return await self.export_data_to_postgres(
+            {
+                **input_data,
+                'data': data,
+            }
+        )
+
+    @activity.defn(name='cleanup_minio_objects_expired')
+    async def cleanup_minio_objects_expired(self, input_data: dict[str, Any]) -> dict[str, Any]:
+        """
+        Delete objects under the given prefixes that are older than the retention window.
+
+        Args (input_data):
+            metadata (dict): Workflow metadata for logging and metrics.
+            prefixes (list[str]): Key prefixes to scan (one level or subtree per prefix).
+
+        Returns:
+            dict[str, Any]: ``success``, ``deleted_count``, and optional ``message``.
+        """
+        if self.minio_repository is None:
+            raise ValueError('Minio repository not initialized')
+
+        metadata = input_data.get('metadata', {})
+        payload = MinioDataFramePayload.from_dict(input_data['data'])
+        prefix = payload.cleanup_prefix()
+        base = now()
+        cutoff = (base.replace(tzinfo=None) if base.tzinfo else base) - timedelta(
+            hours=self.retention_hours
+        )
+
+        report: dict[str, Any] = {
+            'failed': {},
+            'deleted': {},
+            'failed_count': 0,
+            'deleted_count': 0,
+        }
+        try:
+            keys = await self.minio_repository.list_objects(
+                prefix=prefix,
+                recursive=True,
+                metadata=metadata,
+            )
+            for key in keys:
+                try:
+                    ts = MinioDataFramePayload.parse_object_timestamp(key)
+                    if ts is None:
+                        continue
+                    if ts >= cutoff:
+                        continue
+                    await self.minio_repository.delete_file(
+                        object_name=key,
+                        metadata=metadata,
+                    )
+                except Exception as e:
+                    report['failed'][key] = {
+                        'success': False,
+                        'message': str(e),
+                    }
+                    report['failed_count'] += 1
+                    continue
+                report['deleted'][key] = {
+                    'success': True,
+                    'message': 'Deleted',
+                }
+                report['deleted_count'] += 1
+        except Exception as e:
+            trace = traceback.format_exc()
+            await self.send_notification_async(
+                metadata=metadata,
+                notification_id='ERROR_CLEANUP_MINIO_OBJECTS_EXPIRED',
+                message=f'Error cleaning up MinIO objects: {e}',
+                block='cleanup_minio_objects_expired',
+                level=NotificationLevel.ERROR,
+                attachment_content=trace,
+            )
+            self.error(trace, metadata)
+        else:
+            # Cleanup success is expected in normal flow; avoid noisy INFO notifications
+            # that do not impact behavior and can flood observability in test runs.
+            self.info('MinIO objects cleaned up successfully', metadata)
+
+        return report
+
+    def close(self) -> None:
+        """Close Storage resources (MinIO client and Postgres engine)."""
+        Postgres.close(self)
+        MinioManager.close(self)
+
+    def __del__(self):
+        self.close()