Panel Discussion
summary
Panelists argue MLOps differs from DevOps because models are probabilistic, tightly coupled to data quality and versioning, and can produce different outputs for the same code; teams need feature store solutions, reproducible data and model lineage, and monitoring for drift. Practical wins include using Kafka streams for realtime training pipelines, choosing managed options like SageMaker or Metaflow early, and launching with a one‑use‑case MVP (experimentation, serving, or feature reuse) before building a full platform.
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chapters (9)
- 0:00Welcome to Ammo Ops panel
- 0:23Introducing panelists and goals
- 3:10MLOps versus traditional engineering
- 7:03Team boundaries and data contracts
- 11:18MLOps and DevOps principles
- 19:06Adopting MLOps practices
- 21:58Building MLOps platforms
- 27:14Scaling MLOps and platform roles
- 30:09Closing remarks and Q&A
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