MLOps Observability Platform

A production-grade platform for monitoring, validating, and maintaining ML models in real-world environments with comprehensive observability and alerting.

🚀 Features

• Model Observability: Comprehensive metrics collection for model performance monitoring
• Data Validation: Schema validation and data quality checks for production traffic
• Drift Detection: Statistical methods to detect and quantify data drift in real-time
• Model Registry: Version management and lifecycle tracking for ML models
• Performance Visualization: Pre-built Grafana dashboards for real-time monitoring
• Alerting: Configurable alerts for model degradation and data quality issues
• Testing Framework: End-to-end and integration test suites to validate platform functionality

🚧 Architecture

This platform implements a layered architecture:

1. API Layer: FastAPI for model serving and monitoring endpoints with built-in observability
2. Validation Layer: Automated schema and data drift checks for data quality assurance
3. Monitoring Layer: Prometheus for metrics collection and storage with custom metrics
4. Model Registry: MLflow for versioning, metadata, and lifecycle management
5. Visualization Layer: Grafana dashboards with performance visualization and alerting

The architecture follows best practices for observability with separation of concerns and modular design:

📦 Project Structure

mlops-observability/
├── README.md
├── architecture.png
├── docker-compose.yml
├── src/
│   ├── api/
│   ├── monitoring/
│   ├── data_validation/
│   ├── model_registry/
│   └── dashboard/
└── tests/

Model Registry

The Model Registry component provides a centralized repository for model versioning, metadata tracking, and lifecycle management. Key features include:

• Model versioning and storage
• Model metadata and lineage tracking
• Performance metrics comparison between versions
• Stage transitions (development → staging → production)
• Integration with monitoring systems for observability

Usage Example

from src.model_registry.client import ModelRegistry
from src.model_registry.version import compare_model_versions

# Initialize registry client
registry = ModelRegistry(tracking_uri="http://mlflow-server:5000")

# Register a new model
model_uri = registry.register_model(
    model_path="s3://models/model.pkl",
    name="fraud_detection",
    tags={"algorithm": "xgboost", "owner": "data-science-team"}
)

# Compare different model versions
comparison = compare_model_versions(
    registry,
    model_name="fraud_detection",
    version1="1",
    version2="2",
    metric="auc"
)

⚙️ Installation & Setup

Prerequisites

• Docker and Docker Compose
• Python 3.8+
• Git

Quickstart

1. Clone the repo:

   git clone https://github.com/hmm29/mlops-observability.git
   cd mlops-observability

2. Start the monitoring stack:

   docker-compose -f docker/docker-compose-grafana.yml up -d

3. Access services:

   • API: http://localhost:8000
   • Grafana: http://localhost:3000 (login: admin/admin)
   • Prometheus: http://localhost:9090

For detailed setup instructions, see SETUP.md.

📝 Implementation Highlights

Model Monitoring Components

• Comprehensive Metrics Collection:

  • Real-time performance metrics tracking
  • Request volume and latency monitoring
  • Error tracking and categorization

• Data Quality Monitoring:

  • Feature drift detection using KS tests
  • Input validation and schema enforcement
  • Automated anomaly detection

• Visualization & Alerting:

  • Interactive Grafana dashboards with performance panels
  • Real-time alerting for drift detection
  • Customizable notification channels

📝 Current Status

✅ Completed Components:

• Model Registry implementation
• Data Validation (Drift Detection and Schema Validation)
• Unit tests for model registry
• Metrics Collection Service
  • Prometheus metrics collector
  • Model performance metrics
  • System health monitoring
• API Endpoints
  • FastAPI model serving
  • Prediction monitoring
  • Swagger UI documentation
• Dashboard implementation
  • Grafana dashboards for model monitoring
  • Performance visualization panels
  • Data drift monitoring
• Alerting configuration
  • Feature drift alerts
  • High error rate detection
  • Latency threshold monitoring
• Comprehensive Testing
  • Integration tests
  • End-to-end tests
  • Unit tests for model registry
• Documentation
  • API documentation
  • Setup instructions
  • Deployment guides

📝 Next Steps

1. Extended Monitoring Features:

   • A/B testing support
   • Multi-model comparison dashboards
   • Custom user-defined metrics

2. Enhanced Testing:

   • Load testing framework
   • Chaos engineering tests
   • Continuous integration pipelines

3. Advanced Features:

   • Automated model retraining triggers
   • Custom notification channels
   • Advanced data quality monitoring

🧹 Testing Framework

Integration Tests

Our integration tests validate component interactions:

# Run integration tests
pytest tests/test_integration.py

These tests verify:

• API prediction endpoints
• Input validation mechanisms
• Metrics collection
• Drift detection algorithms

End-to-End Tests

Complete system validation is done through E2E tests:

# Run E2E tests
pytest tests/test_e2e.py

These tests confirm:

• Full prediction workflow from request to visualization
• Data drift detection and alerting
• Grafana dashboard accessibility
• System resilience under various conditions

Detailed testing documentation is available in tests/README.md.

🔍 Usage

Making Predictions

import requests
import json

# Send prediction request to the API
url = "http://localhost:8000/predict"
payload = {
    "features": {
        "feature1": 0.5,
        "feature2": 1.0,
        "feature3": "category_a"
    },
    "request_id": "test-123"
}

response = requests.post(url, json=payload)
prediction = response.json()
print(prediction)

Model Drift Monitoring

from src.monitoring.metrics import MLMetricsCollector
from src.data_validation.drift import DriftDetector
import pandas as pd

# Initialize metrics collector
metrics = MLMetricsCollector(model_name="fraud_detection", version="1.0")

# Track model performance
with metrics.track_predictions():
    predictions = model.predict(features)
    
# Check for data drift
detector = DriftDetector(reference_data=training_data)
results = detector.detect_drift(current_data, threshold=0.05)

if results["drift_detected"]:
    print(f"Drift detected in features: {results['flagged_features']}")

Registering a New Model

from src.model_registry.client import ModelRegistry

registry = ModelRegistry(tracking_uri="http://mlflow-server:5000")
model_uri = registry.register_model(
    model_path="path/to/model.pkl",
    name="fraud_detection",
    tags={"algorithm": "xgboost", "version": "1.0.0"}
)

👍 Development

Directory Structure

mlops-observability/
├── docker/                 # Docker configuration files
│   ├── prometheus/         # Prometheus configuration
│   └── grafana/            # Grafana provisioning
├── grafana/                # Grafana dashboards and alerts
│   ├── dashboards/         # Dashboard templates
│   └── alerts/             # Alert configurations
├── src/                    # Source code
│   ├── api/                # FastAPI service
│   ├── monitoring/         # Metrics collection
│   ├── data_validation/    # Schema and drift detection
│   └── model_registry/     # Model versioning
└── tests/                  # Test suite
    ├── model_registry/     # Unit tests
    ├── test_integration.py # Integration tests
    └── test_e2e.py         # End-to-end tests

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Implement your changes
4. Add tests for your implementation
5. Update documentation as needed
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

💡 Conclusion

The MLOps Observability Platform provides a comprehensive solution for monitoring ML models in production environments. With real-time performance tracking, data quality monitoring, and automated alerting, it helps teams maintain reliable AI systems at scale.

Key benefits:

• Early detection of model degradation
• Proactive data quality management
• Streamlined MLOps workflows
• Enhanced model reliability and user trust

For detailed setup and usage instructions, refer to SETUP.md.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.