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Best Practices
Continuous Integration (CI): Automate testing and validation for code, data, and models before deployment.
Continuous Delivery/Deployment (CD): Automate the deployment of the complete ML pipeline and the trained model to production environments (often using Docker/Kubernetes).
Continuous Training (CT): Implement automated triggers to retrain models based on performance degradation (drift) or arrival of significant new data.
Version Control: Use Git for code and configuration. Crucially, version control datasets (Data Versioning) and model artifacts (Model Registry).
Reproducibility: Log all experiment metadata—including hyperparameters, package dependencies, and data/code versions—to enable exact reproduction of any past result.
Infrastructure as Code (IaC): Manage all compute resources and environments (e.g., training clusters, deployment services) using code (e.g., Terraform) for consistency.
Continuous Monitoring: Track both operational metrics (latency, throughput, resource usage) and model performance metrics (accuracy, precision, business KPIs) in production.
Drift Detection: Actively monitor for Data Drift (input data changes) and Concept Drift (target relationship changes) and set up automated alerts and retraining workflows.
Data Validation: Implement continuous checks on the schema, quality, and statistical properties of input data streams before they reach the model.
Model Governance & Lineage: Maintain a clear audit trail of every model, documenting who trained it, when, and with what specific assets, for regulatory compliance and debugging.
Modular Pipelines: Break the ML workflow (data ingestion, preprocessing, training, evaluation, deployment) into independent, reusable components.
Feature Stores: Use a centralized platform to define, serve, and share reusable features across different models and teams, ensuring consistency between training and serving.
Collaboration: Facilitate smooth handoffs and shared ownership between Data Scientists, ML Engineers, and Operations teams through common tools and standardized interfaces.