Code import - branch 0.5.0

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)