DVC: Data Versioning and ML Experiments on Top of Git
Offered By: Toronto Machine Learning Series (TMLS) via YouTube
Course Description
Overview
Explore data versioning and machine learning experiment tracking using DVC (Data Version Control) in this 35-minute conference talk by Dmitry Petrov, Co-Founder & CEO of Iterative Inc., presented at the Toronto Machine Learning Series. Learn how DVC addresses the limitations of popular ML experimentation and metrics logging tools by providing reproducibility through integrated tracking of source code, training data, and metrics within Git repositories. Discover how this open-source tool efficiently manages data and models for hundreds of experiments using codification and metafiles, making it a feasible and effective approach for ML researchers and engineers.
Syllabus
DVC Data Versioning and ML Experiments on Top of Git
Taught by
Toronto Machine Learning Series (TMLS)
Related Courses
End to End Deep Learning Project Using MLOPS DVC Pipeline With Deployments Azure and AWS - Krish NaikKrish Naik via YouTube Open Source Tools for ML Experiments Management
Linux Foundation via YouTube Automating Machine Learning Workflow with DVC
EuroPython Conference via YouTube Transforming a Jupyter Notebook into a Reproducible Pipeline for ML Experiments
PyCon US via YouTube End-to-End Deep Learning Project: Kidney Disease Classification with MLflow, DVC, and Deployment
Krish Naik via YouTube