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Welcome & the ML-to-Production Journey on GCP
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Classroom Contents
Building Scalable, Observable MLOps Systems on Google Cloud
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- 1 Welcome & the ML-to-Production Journey on GCP
- 2 Why Production ML Is Hard
- 3 The 4 Core Barriers: Infra, Tooling, Drift, Governance
- 4 End-to-End MLOps Framework on Google Cloud
- 5 GCP Managed Service Stack: Vertex AI, Dataflow, Cloud Run, BigQuery
- 6 Choosing Deployment Patterns: Real-time vs Batch vs Streaming
- 7 Automotive Case Study: Oil Change Prognostics Architecture
- 8 Production Observability: Seeing Model + System Health
- 9 Model Monitoring Deep Dive: Drift, Skew, Performance Loops
- 10 Infrastructure Telemetry & Incident Response in Practice
- 11 CI/CD for Models: Progressive Rollouts, Monitoring & Rollback
- 12 Governance, Retraining Pipelines & Human-in-the-Loop Approvals
- 13 Wrap-Up: Blueprint to Scale Reliable MLOps
