From Model to Production: Building Scalable MLOps Pipelines
summary
Enterprise MLOps roadmap that moves a team from ad hoc to Level 3 automation in 18 months with concrete results: reduced deployment times 45 days to 4 hours, 94% fewer production issues, and 3.5 million daily inferences at 99.99% uptime with sub 50 ms latency. Implements four pillars: automated model validation (accuracy, latency, explainability gates), CI/CD with dataset and artifact versioning, monitoring for drift (28 critical incidents caught year one) and scalable infra using Kubernetes, Spark, canary and shadow deployments, model registry and feature stores.
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chapters (11)
- 0:00Introduction and Overview
- 1:21Challenges in Enterprise ML Deployment
- 2:02ML Ops Maturity Journey
- 2:44Four Pillars of ML Ops Architecture
- 3:19Automated Validation Framework
- 4:06CI/CD Pipeline
- 4:49Monitoring and Infrastructure
- 6:10Cross-Functional Collaboration
- 7:16Roadmap for Implementing ML Ops
- 8:11Common Pitfalls and Summary
- 9:18Conclusion
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