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
Tracking Data Files and Directories
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- DVC add Command Reference, Iterative, Inc., 2024 - Official documentation for the
dvc addcommand, detailing its usage and available options. - How DVC Works, Iterative, Inc., 2024 (Iterative, Inc.) - Explains the fundamental concepts behind DVC, including data caching,
.dvcfiles, and its integration with Git. - Introducing MLOps: How to go from Model to Production, Mark Treveil, Nicolas Omont, Aurélien Géron, Clement Stenac, Mickaël Bertrand, Patrick Brady, Aditya Joshi, Michel Lutz, Hervé Siby, Rik van der Burgt, Kevin Van der Burgt, Roel Van der Burgt, Manon Van der Burgt, and Alice Zheng, 2022 (O'Reilly Media) - Provides a comprehensive overview of MLOps practices, including data management and versioning strategies, setting context for tools like DVC.