An introduction to MLOps with TensorFlow Extended (TFX)
summary
An introduction of TensorFlow Extended shows how TFX pipelines automate repeatable training and deployment, with ML Metadata recording every artifact so teams can compare model and data changes over time. The system deploys to TF Serving, Vertex Prediction, TF Lite and TFJS, runs from laptop to cluster, supports XGBoost/PyTorch containers, integrates with Feast or Vertex feature store, and claims over 10 million downloads as evidence of broad use.
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chapters (7)
- 0:00Introduction to MLOps
- 0:33MLOps Pillars: Process & Change
- 3:01The Challenge of Data Drift
- 4:41Introduction to TFX
- 7:25TFX Components and Metadata
- 10:18TFX Versatility and Scaling
- 11:21TFX Resources and Community
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