CourseML System Design · 0516 minPremium
Training pipelines
How a model gets built, rebuilt and trusted: the pipeline from snapshot to registry, batch versus continuous training and how to choose a cadence from a staleness curve, warm starts, distributed training for large embedding tables, sizing a training run, experiment tracking, reproducibility and the validation gates every model passes before it can serve.
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What’s inside
- 1. The pipeline
- 2. How often to retrain
- 3. Warm start or from scratch?
- 4. Distributed training
- 5. Experiment tracking and reproducibility
- 6. Validation gates
- What goes wrong
- Drills
- Check yourself