Data Version Control with DVC

SKU: M2107
4 Lesson
|
4 Hours
Data version control is critical in machine learning projects. This course explains how DVC helps track datasets, experiments and pipelines to make ML projects reproducible and organized.
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MLOps Data version control

Overview

Prerequisites

  • Basic Git knowledge
  • Understanding of ML workflows
  • Basic Python knowledge
  • Familiarity with command line
  • Interest in managing ML datasets

What Will You Learn

  • Why data versioning is important
  • How DVC works with Git
  • Tracking datasets in ML projects
  • Creating reproducible ML pipelines
  • Managing experiment results
  • Remote storage for datasets
  • Best practices for ML data management

Key Features

Course Curriculum

1. Why data versioning is important
2. Problems with dataset management
3. Overview of DVC
4. DVC architecture
1. Initializing DVC
2. Tracking datasets
3. Managing large files
4. Data pipelines
1. Running ML experiments
2. Tracking experiment results
3. Comparing experiments
4. Reproducibility
1. Integrating DVC with Git
2. Remote data storage
3. Team collaboration
4. Best practices
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1-800-7430-173 (US Toll Free)
+91 7240740740 (India)
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Course Fees

Online Class Room Program

US $ 199.00
100% Money Back Guarantee
  • Duration : 4 Hrs
  • Plus Self Paced

Classes Starting From

  • Fast Track Batch 29 Jun 2026
  • Weekday Batch 29 Jun 2026
  • Weekend Batch 04 Jul 2026

Corporate Training

Corporate Training
  • Customized Training Delivery Model
  • Flexible Training Schedule Options
  • Industry Experienced Trainers
  • 24x7 Support
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