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Managing the Complete Machine Learning Lifecycle with MLflow - Part 2
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- 1 Intro
- 2 Data Preparation
- 3 Artifacts
- 4 Parameters
- 5 HyperParameter Optimization
- 6 Randomized Search
- 7 Community Edition
- 8 What model should we put to production
- 9 MLflow Client
- 10 Model URI
- 11 UDF
- 12 Running the model in production
- 13 Data silos
- 14 Defining the experiment path
- 15 Evaluating the current model
- 16 Exercise
- 17 MLflow Model
- 18 Questions
