Building Scalable, Observable MLOps Systems on Google Cloud
summary
Blueprint for running models on Google Cloud with concrete pieces: use Vertex AI for training and experiment tracking, Dataflow for real‑time feature pipelines, Cloud Run for containerized inference, and BigQuery for TB–PB logging and offline analysis. Detect data and prediction drift, trigger retraining, then deploy with CI/CD progressive rollouts (canary 5%, then 5→25→50→100%), monitor p95 latency, error rates and model A/B metrics, roll back automatically when alerts show performance loss.
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chapters (13)
- 0:00Welcome & the ML-to-Production Journey on GCP
- 1:44Why Production ML Is Hard
- 3:02The 4 Core Barriers: Infra, Tooling, Drift, Governance
- 5:03End-to-End MLOps Framework on Google Cloud
- 6:12GCP Managed Service Stack: Vertex AI, Dataflow, Cloud Run, BigQuery
- 8:14Choosing Deployment Patterns: Real-time vs Batch vs Streaming
- 9:40Automotive Case Study: Oil Change Prognostics Architecture
- 11:45Production Observability: Seeing Model + System Health
- 12:48Model Monitoring Deep Dive: Drift, Skew, Performance Loops
- 15:36Infrastructure Telemetry & Incident Response in Practice
- 17:41CI/CD for Models: Progressive Rollouts, Monitoring & Rollback
- 19:26Governance, Retraining Pipelines & Human-in-the-Loop Approvals
- 21:42Wrap-Up: Blueprint to Scale Reliable MLOps
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