Mastering MLOps: Building Future Ready ML Systems
summary
Claims that most ML projects never reach production and that Google has pushed thousands of models into production, then maps a three-level maturity model (manual → pipelineized → automated CI/CD/CT) showing most orgs at level zero, few at level one and pockets of level two. Concrete tooling and patterns: Vertex AI, Feature Store, KFP/Kubeflow, Jenkins, model registry and monitoring, plus rule-driven automated retraining triggers tied to support-ticket or metric thresholds; Google reports this scales in practice.
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chapters (9)
- 0:00Introduction to MLOps
- 3:10Why MLOps matters
- 6:22Core MLOps concepts
- 9:54Maturity levels overview
- 16:36Challenges in production
- 20:08Level 1 automation
- 22:33Level 2 and 3 automation
- 23:57Vertex AI ecosystem
- 27:26Q&A and resources
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