ML Engineering

This section covers operational practices (MLOps), deployment infrastructure, model monitoring, and software engineering best practices for ML codebases.

Key Focus Areas

• MLOps & Orchestration: CI/CD for machine learning, pipeline orchestration (Kubeflow, Airflow).
• Model Deployment: Containerization (Docker), Triton Inference Server, ONNX runtime optimization, edge deployment.
• Monitoring & Governance: Data quality monitoring, concept drift detection, model explainability.