Building Production ML Pipelines: MLOps Best Practices

# Building Production ML Pipelines: MLOps Best Practices Taking ML models from notebooks to production requires robust pipelines. MLOps brings DevOps practices to machine learning. ## The ML Pipeline ### 1. Data Ingestion Automated data collection with validation: - Schema validation - Data quality checks - Anomaly detection ### 2. Feature Engineering Consistent, versioned feature pipelines: - Feature stores - Feature versioning - Online/offline features ### 3. Model Training Reproducible training with: - Experiment tracking - Hyperparameter logging - Model versioning ### 4. Model Validation Automated validation before deployment: - Performance metrics - Fairness checks - Regression tests ### 5. Deployment Automated deployment with: - Canary releases - A/B testing - Rollback capability ### 6. Monitoring Continuous monitoring for: - Model drift - Data drift - Performance degradation ## Tools and Platforms ### Experiment Tracking - MLflow - Weights & Biases - Neptune ### Feature Stores - Feast - Tecton - Hopsworks ### Model Registry - MLflow Model Registry - Vertex AI Model Registry - SageMaker Model Registry ### Orchestration - Airflow - Kubeflow - Prefect ## Best Practices 1. **Version everything**: Code, data, models, configs 2. **Automate testing**: Unit, integration, model tests 3. **Monitor continuously**: Detect issues before users do 4. **Document pipelines**: Future you will thank you ## Conclusion MLOps is essential for sustainable ML. Start simple and add complexity as your needs grow.