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MLOps with MLflow and Kubernetes
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A practical MLOps book covering experiment tracking, model registries, feature pipelines, CI/CD for ML, Kubernetes serving, monitoring, drift, governance, and safe model rollout.
Level: intermediateLanguage: EN EnglishRating: 0Reviews: 0
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Book contents
01Chapter 1: Why Machine Learning Needs Operations02Chapter 2: Datasets, Features, Models, and Artifacts03Chapter 3: Experiment Tracking with MLflow04Chapter 4: Model Registries and Approval Workflows05Chapter 5: Reproducible Training Pipelines06Chapter 6: Feature Stores and Data Versioning07Chapter 7: CI/CD for Machine Learning Projects08Chapter 8: Containerizing Models for Deployment09Chapter 9: Model Serving on Kubernetes10Chapter 10: Batch vs Real-Time Inference11Chapter 11: Monitoring Drift, Quality, and Latency12Chapter 12: Rollback, Shadow Deployment, and Canary Models13Chapter 13: Track and Compare Model Experiments14Chapter 14: Deploy a Model to Kubernetes15Chapter 15: Build a Model Monitoring Dashboard16Chapter 16: Operate a Safe Model Release Process17Appendix E: Deeper Practice and Extra Examples
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