MLOps Training (Class-3): Data Version Control with DVC & Git
MLOps Training (Class-3): Data Version Control with DVC & Git
🚀 Master MLOps with this complete DVC (Data Version Control) tutorial! Learn how to version your large datasets and integrate it seamlessly with Git for reproducible machine learning projects. This is Class- 3 of our end-to-end MLOps series.
Hands-On Steps Covered:
00:00 Introduction & Why DVC?
02:00 The Problem with GitHub for Data
05:00 Installing DVC
08:00 DVC Init & Project Setup
10:00 DVC Add & Git Integration
15:00 Tracking Data Changes with DVC
20:00 Reverting Code and Data to a Previous Commit
25:00 Advanced Demo: Adding and Deleting Data Files
30:00 How DVC Cache Works (Local Storage)
35:00 Summary & Next Steps (Cloud Storage with AWS/GCP)
🔗Playlist Link: • Complete MLOps Course for Beginners (2026)...
In this video, we solve a critical problem in Machine Learning: how to handle and version large datasets that are too big for GitHub. We dive deep into DVC, the industry-standard tool for data versioning.
✨ What you'll learn in this session:
• Why GitHub is NOT designed for large datasets (GBs of data).
• The importance of Data Versioning in the MLOps lifecycle.
• How to install and initialize DVC in your project.
• Hands-on demo: Using `dvc init`, `dvc add`, and `dvc status`.
• How DVC and Git work together (the magic of `.dvc` files).
• Tracking changes in your data: adding, modifying, and deleting files.
• Reverting to previous versions of both your code AND data with `git checkout` and `dvc checkout`.
• Understanding how DVC stores data locally using MD5 hashes in the cache.
Recommended Next Videos:
• Class 1: • MLOps Training (Class-1) – What is MLOps? ...
• Class 2: • MLOps Training (Class-2): ML Lifecycle & G...
💡 Who is this for?
Aspiring Data Scientists and ML Engineers
Software Engineers moving into MLOps
Anyone who wants to learn professional best practices for managing ML projects
Subscribe for more tutorials on MLOps, Machine Learning, and Data Science!
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Topic covered:
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ai
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