Why you need to think about MLOps at the beginning of your project
summary
A practical roadmap that argues for building MLOps from day one with concrete tradeoffs: projects that skip it hit one month refactor surprises when notebooks become production, while choosing among Level 0/1/2 maturity sets how much automation and CI/CD to budget. Implements CI/CD, model serving, continuous training and monitoring, recommends k‑fold or multi‑armed‑bandit testing, and flags real failure modes—data leakage, missing training artifacts, model drift—and quantifies benefits in faster time‑to‑market and lower cloud costs.
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chapters (12)
- 0:00MLOps importance
- 2:20MLOps challenges
- 3:45MLOps projects overview
- 5:51MLOps definition
- 7:17MLOps project phases
- 8:23Data science team maturity
- 11:41MLOps penetration level
- 12:24MLOps project benefits
- 15:40Model testing strategies
- 17:06Problems without MLOps
- 19:14Data drift explained
- 20:39Summary
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