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SIENTIAPDE-1182

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Update README.md to enhance architecture documentation

- Removed detailed system overview diagram to streamline content.
- Added architecture diagrams for key workflows: PredictionsBatch, PredictionProcess, FormatAndExportPrediction, and MinimalRetrain.
- Improved clarity and structure of the architecture principles section.
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vitor-aignosi
2025-08-29 16:59:00 -03:00
parent d37a53f5ea
commit f9784b8f3e
1 changed files with 41 additions and 89 deletions
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README.md
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@@ -23,95 +23,6 @@ A high-performance, scalable machine learning prediction system built on Tempora
The Laborious system uses a Temporal-based workflow architecture with clear separation of concerns and robust error handling. The architecture is designed for high availability, scalability, and operational excellence in production ML environments. The Laborious system uses a Temporal-based workflow architecture with clear separation of concerns and robust error handling. The architecture is designed for high availability, scalability, and operational excellence in production ML environments.
### System Overview
```
┌─────────────────────────────────────────────────────────────────────────────────┐
│ Temporal Cluster │
│ ┌─────────────────┐ ┌──────────────────┐ ┌─────────────────────────┐ │
│ │ Main Worker │ │ Temporal Client │ │ Task Queues │ │
│ │ │◄──►│ │◄──►│ │ │
│ │ - Metrics Server│ │ - Namespace Mgmt │ │ - predictions_batch-queue│ │
│ │ - Notifications │ │ - Runtime Config │ │ - minimal_retrain-queue │ │
│ │ - Lifecycle │ │ - Connection │ │ - Auto-scaling │ │
│ │ - Health Checks │ │ - Security │ │ - Load Balancing │ │
│ └─────────────────┘ └──────────────────┘ └─────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ Workflow Layer │
│ ┌─────────────────┐ ┌─────────────────────────────┐ │
│ │ PredictionsBatch│ │ Sub-Workflows │ │
│ │ │ │ │ │
│ │ - Data Loading │ │ - PredictionProcess │ │
│ │ - Configuration │ │ - FormatAndExportPrediction │ │
│ │ - Delegation │ │ - Error Handling │ │
│ └─────────────────┘ └─────────────────────────────┘ │
│ ┌─────────────────┐ ┌─────────────────────────────┐ │
│ │ MinimalRetrain │ │ Model Management │ │
│ │ │ │ │ │
│ │ - Retraining │ │ - Version Control │ │
│ │ - Validation │ │ - Production Updates │ │
│ │ - Deployment │ │ - Quality Assurance │ │
│ └─────────────────┘ └─────────────────────────────┘ │
└─────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ Activity Layer │
│ ┌─────────────────┐ ┌─────────────────────────────┐ │
│ │ Data Quality │ │ MLFlow Operations │ │
│ │ │ │ │ │
│ │ - Input Gates │ │ - Model Transform │ │
│ │ - Validation │ │ - Model Prediction │ │
│ │ - Filtering │ │ - Response Validation │ │
│ │ - Policy Mgmt │ │ - Error Handling │ │
│ └─────────────────┘ └─────────────────────────────┘ │
│ ┌─────────────────┐ ┌─────────────────────────────┐ │
│ │ Storage Ops │ │ OPC Operations │ │
│ │ │ │ │ │
│ │ - PostgreSQL │ │ - Server Connections │ │
│ │ - Data Export │ │ - Tag Writing │ │
│ │ - Metrics │ │ - Real-time Export │ │
│ │ - Cleanup │ │ - Error Recovery │ │
│ └─────────────────┘ └─────────────────────────────┘ │
└─────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ Data Services │
│ ┌─────────────────┐ ┌─────────────────────────────┐ │
│ │ PostgreSQL │ │ MongoDB │ │
│ │ │ │ │ │
│ │ - Predictions │ │ - Notifications │ │
│ │ - Metadata │ │ - Audit Logs │ │
│ │ - Metrics │ │ - Configuration │ │
│ │ - Cleanup │ │ - User Management │ │
│ └─────────────────┘ └─────────────────────────────┘ │
│ ┌─────────────────┐ ┌─────────────────────────────┐ │
│ │ MLFlow API │ │ OPC Servers │ │
│ │ │ │ │ │
│ │ - Model Serving │ │ - Real-time Data │ │
│ │ - Transform │ │ - Industrial Integration │ │
│ │ - Prediction │ │ - Security & Auth │ │
│ │ - Versioning │ │ - Load Balancing │ │
│ └─────────────────┘ └─────────────────────────────┘ │
└─────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ External Systems │
│ ┌─────────────────┐ ┌─────────────────────────────┐ │
│ │ Prometheus │ │ Kubernetes │ │
│ │ │ │ │ │
│ │ - Metrics │ │ - Orchestration │ │
│ │ - Alerting │ │ - Scaling │ │
│ │ - Dashboards │ │ - Health Checks │ │
│ │ - Monitoring │ │ - Resource Management │ │
│ └─────────────────┘ └─────────────────────────────┘ │
└─────────────────────────────────────────────────────────┘
```
### Architecture Principles ### Architecture Principles
@@ -284,6 +195,14 @@ The **PredictionsBatch** workflow is the main entry point for batch prediction p
} }
``` ```
#### Architecture Diagram
```mermaid
flowchart LR
A[1. load_custom_query] --> B[2. prediction_process 🔃]
A -.-> Database[(Database)]
```
### 2. Prediction Process Workflow (`prediction_process.py`) ### 2. Prediction Process Workflow (`prediction_process.py`)
The **PredictionProcess** workflow implements the core prediction pipeline for ML model inference. It handles data quality validation, MLFlow model interactions, and prediction processing. The **PredictionProcess** workflow implements the core prediction pipeline for ML model inference. It handles data quality validation, MLFlow model interactions, and prediction processing.
@@ -345,6 +264,17 @@ The **PredictionProcess** workflow implements the core prediction pipeline for M
} }
``` ```
#### Architecture Diagram
```mermaid
flowchart LR
A[1. get_last_timestamp] --> B[2. input_gate] --> C[3. request_transform] --> D[4. mlflow_response_gate] --> E[5. mlflow_content_gate] --> F[6. request_predict] --> G[7. mlflow_response_gate] --> H[8. format_and_export_prediction🔃]
A -.-> Redis[(Redis)]
C -.-> MLFlow[MLFlow]
F -.-> MLFlow[MLFlow]
G -.-> Filters[MLFlow Filters]
```
### 3. Format and Export Prediction Workflow (`format_and_export_prediction.py`) ### 3. Format and Export Prediction Workflow (`format_and_export_prediction.py`)
The **FormatAndExportPrediction** workflow handles prediction data formatting and export operations to multiple destinations. The **FormatAndExportPrediction** workflow handles prediction data formatting and export operations to multiple destinations.
@@ -368,6 +298,17 @@ The **FormatAndExportPrediction** workflow handles prediction data formatting an
- **Performance Monitoring**: Comprehensive metrics for export operations - **Performance Monitoring**: Comprehensive metrics for export operations
- **Error Handling**: Robust error handling with notification integration - **Error Handling**: Robust error handling with notification integration
#### Architecture Diagram
```mermaid
flowchart LR
A[1. format_prediction/format_default_prediction] --> B[2. write_opc_data] --> C[3. export_data_to_postgres] --> D[4. write_metrics]
A -.-> Format[Data Formatting]
B -.-> OPC[OPC Servers]
C -.-> PostgreSQL[(PostgreSQL)]
D -.-> Prometheus[Prometheus]
```
### 4. Minimal Retrain Workflow (`minimal_retrain.py`) ### 4. Minimal Retrain Workflow (`minimal_retrain.py`)
The **MinimalRetrain** workflow handles automated model retraining and production model updates. The **MinimalRetrain** workflow handles automated model retraining and production model updates.
@@ -385,6 +326,17 @@ The **MinimalRetrain** workflow handles automated model retraining and productio
4. **Production Update**: Updates production model if quality criteria met 4. **Production Update**: Updates production model if quality criteria met
5. **Data Export**: Exports training data for analysis 5. **Data Export**: Exports training data for analysis
#### Architecture Diagram
```mermaid
flowchart LR
A[1. load_custom_query] --> B[2. retrain_model] --> C[3. update_production_model] --> D[4. export_data_to_postgres]
A -.-> Database[(Database)]
B -.-> MLFlow[MLFlow]
C -.-> MLFlow[MLFlow]
D -.-> PostgreSQL[(PostgreSQL)]
```
## 📋 Prerequisites ## 📋 Prerequisites
- Python 3.11+ - Python 3.11+