Data Versioning and Experiment Tracking for Machine Learning
Chapter 1: The Need for Reproducibility in Machine Learning
Challenges in Managing ML Projects
Why Git Alone Is Not Sufficient
Defining Reproducibility in ML
Components of a Reproducible ML Workflow
Introduction to Data Versioning Concepts
Introduction to Experiment Tracking Concepts
Quiz for Chapter 1
Chapter 2: Versioning Data with DVC
Data Versioning Strategies
Introducing Data Version Control (DVC)
Setting Up DVC in a Project
Tracking Data Files and Directories
Storing and Retrieving Data Versions
Connecting DVC to Remote Storage (S3, GCS, Azure Blob)
Switching Between Data Versions
Hands-on Practical: Versioning a Dataset
Quiz for Chapter 2
Chapter 3: Tracking Experiments with MLflow
The Importance of Experiment Tracking
Introducing MLflow Tracking
Setting up MLflow
Logging Parameters and Metrics
Logging Artifacts (Models, Plots, Files)
Organizing Runs with Experiments
Using the MLflow UI
Comparing Experiment Runs
Practice: Tracking a Training Run
Chapter 4: Integrating DVC and MLflow for Reproducible Workflows
Connecting Data Versions to Experiments
Structuring Projects for Integration
Logging DVC Metadata in MLflow
Creating DVC Pipelines
Reproducing DVC Pipelines
Tracking DVC Pipeline Metrics
Combining DVC Pipelines and MLflow Tracking
Best Practices for Integrated Workflows
Hands-on Practical: Building an Integrated Pipeline
Connecting Data Versions to Experiments
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- Data Version Control (DVC) Documentation, Iterative, Inc., 2024 - Official guide to DVC, explaining data versioning concepts,
.dvcfiles, and linking data to Git commits. - MLflow Documentation, The MLflow Project, 2024 - Official guide to MLflow, covering experiment tracking, logging parameters, metrics, and tags for runs.
- Designing Machine Learning Systems: An Iterative Process for Production-Ready AI, Chip Huyen, 2022 (O'Reilly Media) - A comprehensive book on MLOps practices, including methods for ensuring reproducibility in ML system design.
- Pro Git, Scott Chacon and Ben Straub, 2014 (Apress) - The definitive online guide for Git, explaining version control, commit hashes, and repository operations.