Productionizing ML with ML Ops and Cloud AI
summary
Explains a concrete MLops stack using Google Cloud: AI Platform Notebooks, Deep Learning VM and Containers, AI Platform Training and Prediction, GKE, BigQuery and Dataflow, plus AI Platform Pipelines for reproducible artifact lineage. Shows standardized images remove setup toil, uses distributed training and preemptible GPUs ~1/3 cost for cheaper large‑batch runs, and reports TensorFlow Enterprise doubles IO throughput and boosts GPU utilization ~30%. Demonstrates an end‑to‑end pipeline that tracks datasets models and hyperparameters and runs in production with autoscaling and monitoring.
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chapters (6)
- 0:00Intro
- 0:38What is ML Ops
- 11:41Standardization
- 17:09ML Ops vs Production
- 22:03Deep Learning Container
- 25:06Performance
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