Are you ready for MLOps? 🫵
summary
Claim: only about half of AI models reach production and another headline figure says ~87% never do; explanation centers on missing foundations not the idea of MLOps. Concrete tooling examples include ml flow, asml, vertex AI, SV maker and orchestration via GI up actions plus cloud monitoring. Emphasis is on which practices are implemented: rigorous CI/CD, dataset/version traceability, model registry, feedback loops and team structure rather than vague promises.
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chapters (6)
- 0:00Introduction
- 1:52Being ready for MLOps
- 8:18Role and promise of MLOps
- 15:28MLOps vs DevOps
- 24:45Building MLOps on DevOps
- 26:14Conclusion and Q&A
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