MLOps — Overview & Principles

MLOps (Machine Learning Operations) applies DevOps principles to the ML lifecycle. It bridges the gap between data scientists (who build models) and engineers (who deploy systems). The core problem: 90% of ML projects never reach production. MLOps fixes the deployment bottleneck with automation, reproducibility, and monitoring.

1. Data Collection & Labeling 2. Data Versioning (DVC) 3. Feature Engineering & Feature Store 4. Experiment Tracking (MLflow, W&B) 5. Model Training (distributed if needed) 6. Model Evaluation & Validation 7. Model Registry & Versioning 8. Deployment (REST API, batch, streaming) 9. Monitoring (data drift, model drift, performance) 10. Retraining Pipeline (triggered by drift or schedule)

Level 0 — Manual. No automation. Jupyter notebooks. Model deployed by copy-pasting code. (90% of teams) Level 1 — ML pipeline automation. Automated training. Feature store. Model registry. Manual deployment trigger. Level 2 — CI/CD pipeline automation. Any code change triggers retraining, evaluation, and deployment automatically. Full MLOps.

Data versioning: DVC, LakeFS Experiment tracking: MLflow, Weights & Biases, Neptune Feature store: Feast, Hopsworks, Tecton Model registry: MLflow Model Registry, HuggingFace Hub Orchestration: Apache Airflow, Prefect, Metaflow, Kubeflow Model serving: FastAPI, TorchServe, Triton Inference Server, BentoML Monitoring: Evidently AI, WhyLogs, Arize, Fiddler CI/CD: GitHub Actions, GitLab CI, Jenkins + DVC Infrastructure: Docker, Kubernetes, AWS SageMaker, Vertex AI