49fa60c66f
Enhance documentation across multiple modules with detailed parameter descriptions and usage examples
96 lines
3.5 KiB
Markdown
96 lines
3.5 KiB
Markdown
# Sientia DataOps Laborious
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The Sientia DataOps Laborious is a Temporal-based workflow application that handles batch predictions and data processing for industrial data. It integrates with MLFlow for model management, PostgreSQL for data storage, and OPC for real-time data output. The module is designed to process data in a reliable and scalable manner using Temporal.io's workflow orchestration capabilities. It's get data from Scouter sinks, process it, make predictions using MLFlow models and generates metrics for the predictions.
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## Key Features
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- Batch predictions using MLFlow models
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- Data transformation and preprocessing
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- Workflow orchestration using Temporal.io
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- Integration with PostgreSQL for data storage
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- OPC integration for real-time data output
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- Comprehensive error handling and notifications
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- Configurable data filters and quality gates
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- Scalable deployment architecture
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## Workflows
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### Predictions Batch
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The main workflow that orchestrates batch predictions. Steps:
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- prepare_activity: Prepares the activity with schedule and model information
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- load_custom_query: Loads data using a custom query
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- prediction_process: Executes the prediction process using the Prediction Process sub-workflow
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#### Workflow inputs:
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- `schedule_name`: The schedule name of the activity
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- `model_name`: The model name of the activity
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- `model_id`: The model id of the activity
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- `query`: The custom query to load data
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- `schema`: The schema of the data
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- `table_name`: The name of the table to process
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- `input_filters`: The filters to be applied during prediction
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- `mlflow_transform_filters`: The filters to be applied during prediction
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- `mlflow_predict_filters`: The filters to be applied during prediction
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- `model_retention`: The model retention period in minutes
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- `path_priority`: The path priority
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### Prediction Process
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Sub-workflow that handles individual prediction processing:
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- get_last_timestamp: Gets the last timestamp of the data
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- input_gate: Filters input data based on configured rules
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- repeat_last_prediction: Repeats the last prediction if the data is empty
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- request_transform: Makes predictions using MLFlow models
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- mlflow_response_gate: Handles prediction or transform responses and filters
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- mlflow_content_gate: Filters transform responses based on configured rules
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- request_predict: Makes predictions using MLFlow models
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- format_and_export_prediction: Formats and exports predictions using the
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Format and Export Prediction sub-workflow
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### Format and Export Prediction
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Sub-workflow that handles prediction formatting and export:
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- format_prediction: Formats prediction data if path flag is None
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- format_default_prediction: Formats default prediction data if path flag is not None
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- export_to_postgres: Exports formatted predictions to PostgreSQL
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- write_to_opc: Writes predictions to OPC server
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## Environment variables
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- `POSTGRES_HOST`
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- `POSTGRES_PORT`
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- `POSTGRES_USER`
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- `POSTGRES_PASSWORD`
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- `POSTGRES_DBNAME`
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- `POSTGRES_MIN_CONNECTIONS`
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- `POSTGRES_MAX_CONNECTIONS`
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- `MLFLOW_HOST`
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- `MLFLOW_PORT`
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- `MLFLOW_USERNAME`
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- `MLFLOW_PASSWORD`
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- `OPC_CONFIG` - json string containing the opc configuration for multiple opc servers
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For single opc server use:
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- `OPC_URL`
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- `OPC_NAME`
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- `OPC_SERVER_URI`
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- `OPC_CERT_PATH`
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- `OPC_PRIVATE_KEY_PATH`
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- `OPC_SERVER_CERT_PATH`
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- `OPC_RECONNECTION_INTERVAL`
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- `TEMPORAL_HOST`
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- `TEMPORAL_NAMESPACE`
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## Application deployment
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The application can be deployed using the following command:
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```bash
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helm upgrade --install sientia-dataops-laborious sientia/sientia-module -n sientia --create-namespace -f ./values.yaml
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```
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