MLFlow in Action: Hands on guide to ML experiments

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Section 1: Getting Started

  1. Need for MLFlow
  2. Download Resources
  3. Installation

Section 2: MLFlow Tracking

  1. Experiment vs Run
  2. Demo of typical Data Science Project (Sklearn Experiment)
  3. MLFlow Tracking Steps

Section 3: Understanding MLruns Directory

  1. Components of MLruns directory (Understanding MLruns Directory)
  2. Tracking UI
  3. Model Registry

Section 4: Advanced Functionalities

  1. Changing tracking Location
  2. Multiple Runs and Experiments (Launch multiple runs)
  3. Autologging
  4. Tracking server
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