Build an End-to-End ML Workflow for Your Organization
summary
Organizations need a centralized data lake, feature store and model registry, plus experiment tracking and GPU/distributed training to get reproducibility and scale. Building an internal platform takes about 3–6 months with a small platform team (roughly 4–8 engineers) and a budget often in the 500k–3M annual range; buying SaaS or using Kubeflow or MLflow gets teams running in minutes. A right stack cuts end‑to‑end cycle times (example: six months to two weeks) and prevents 50–70% of engineer time being spent on stitching code.
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chapters (8)
- 0:00Introduction and context
- 2:00ML operational challenges
- 5:07Technical debt in ML
- 7:06Core platform requirements
- 10:52Current organizational setups
- 13:36Build versus buy decision
- 18:51Recommended ML stack template
- 24:25Q&A and closing
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