Streamlining Machine Learning Operations
summary
Describes how Amazon SageMaker turns a brittle, black‑box ML workflow into an integrated pipeline: SageMaker Debugger halts training and logs tensor state for exploding/vanishing gradients, Model Monitor continuously checks for data drift and alerts, and Ground Truth uses active learning to auto‑label data and routes low‑confidence items to humans. Covers notebooks, Experiments, Autopilot, hyperparameter tuning, batch transform and A2I for human review, and notes SageMaker Studio preview in the Ohio region.
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chapters (13)
- 0:00Introduction
- 0:44ML Development Cycle
- 4:30My ML Journey
- 8:37Operating ML at Scale
- 10:25The Delivery Model
- 13:47Team Size
- 14:42Scale
- 16:39Workflow Orchestration
- 19:49Machine Learning Development Cycle
- 20:15Build Phase
- 26:30Deploy Phase
- 34:15Recap
- 36:33Survey
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