Learn how to build a reproducible machine learning and experiment tracking pipeline using Python and DVC (Data Version Control) in this comprehensive tutorial video. Explore the process of managing machine learning experiments, tracking results, and ensuring complete reproducibility. Dive into practical examples using Scikit-Learn to build and compare linear regression and random forest models on a real dataset. Discover how to integrate DVC into your project, track evaluation metrics, and effectively compare experiment results. Gain valuable insights into best practices for reproducible machine learning workflows and experiment management.
Reproducible Machine Learning and Experiment Tracking Pipeline with Python and DVC
Overview
Syllabus
What is DVC?
Overview of the dataset we're going to use
Start the first Machine Learning experiment - use Linear Regression
Add DVC to the project
Add second experiment to the project - use Random Forest
Compare metrics from both experiments
Taught by
Venelin Valkov
