Continuous Delivery for Machine Learning — The Future of MLOps
summary
Describes concrete MLOps patterns and a practical CI/CD approach for ML: three deployment modes, a model registry with blue‑green swaps, shadow and traffic‑split experiments, and a claim that a three‑day proof‑of‑concept is achievable. Recommends tooling and operational practices: Kafka + Python workers + S3/Gluster for raw traffic retention (~12 hours) and aggregation, plus data versioning, input‑drift metrics, explainability hooks, gated governance and role‑based pipeline controls.
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chapters (7)
- 0:00Introduction
- 3:57MLOps importance
- 7:52Organizational challenges
- 11:46Technical deployment challenges
- 17:25Model deployment methods
- 23:53Model monitoring and management
- 30:55MLOps future
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