Operationalizing Model Lifecycle with MLFlow
summary
MLflow is an open source lifecycle platform that records code data env params metrics and stores artifacts so runs are reproducible; it packages models for different serving flavors and registers models with staging and production including versioning, schema checks, audit trails and promote/rollback workflows. Demo shows sklearn code logging params, metrics and PNG artifacts and the UI plus APIs retrieving a specific run and model. Adoption claims: 2 million monthly downloads, 200+ contributors, 100+ organizations.
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chapters (11)
- 0:00Introduction
- 1:11Databricks
- 3:43Agenda
- 6:45Data Data Everywhere
- 11:35MLFlow Components
- 16:03Model Definition
- 20:25MLFlow Features
- 22:11MLFlow Docs
- 25:01Comparing Experiments
- 26:18Individual Runs
- 27:50Model Registry
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