By completing this course, learners will:

• Version control datasets and ML models using DVC.

• Build reproducible pipelines for ML workflows.

• Manage remote storage for data collaboration.

• Track and compare experiments systematically.

• Integrate DVC with MLOps and CI/CD practices.

• Collaborate effectively on team-based ML projects.

Course Syllabus

Module 1: Introduction to DVC

• What is DVC and why it’s needed

• DVC vs Git: differences and collaboration

• Installing and setting up DVC

Module 2: Core Concepts

• Data versioning basics

• Tracking large files with .dvc files

• Staging, committing, and pushing with Git + DVC

Module 3: Remote Storage

• Configuring DVC remotes (S3, GCS, Azure, SSH, local)

• Pushing and pulling datasets/models

• Best practices for remote storage

Module 4: Pipelines & Reproducibility

• Creating DVC pipelines (dvc.yaml)

• Stages, dependencies, and outputs

• Reproducibility across environments

Module 5: Experiment Tracking

• Running and logging experiments

• Comparing results with metrics and plots

• Hyperparameter tuning with DVC

Module 6: Collaboration & Team Workflows

• Sharing data and models in teams

• Git branches + DVC pipelines

• Resolving conflicts in data versioning

Module 7: Integrations

• DVC with Jupyter Notebooks

• MLOps pipelines with DVC

• CI/CD automation (GitHub Actions, GitLab, Jenkins)

Module 8: Advanced Features

• Caching and performance optimization

• Custom pipelines and parameters

• Using DVC with Docker and Kubernetes

Module 9: Real-World Projects

• End-to-end ML workflow with DVC

• Computer vision project with dataset versioning

• NLP pipeline with experiments tracked via DVC

Module 10: Best Practices

• Structuring ML repos with DVC

• Data governance and compliance

• Scaling DVC for enterprise ML

Learners will receive a Certificate of Completion from Uplatz, validating their expertise in DVC and ML workflow management. This certification demonstrates readiness for roles in ML engineering, data science, and MLOps.

DVC skills prepare learners for roles such as:

• Machine Learning Engineer

• Data Scientist

• MLOps Engineer

• Research Engineer

• Data Engineer

With the rise of reproducibility and governance in ML projects, DVC is becoming a must-have tool in the MLOps and data science ecosystem.

1. What is DVC and why is it used?
   DVC is a tool for data, model, and experiment versioning, enabling reproducibility in ML workflows.

2. How does DVC work with Git?
   Git manages code, while DVC tracks large data files and models via .dvc metadata files.

3. What are DVC pipelines?
   Pipelines define stages of ML workflows (data prep, training, evaluation) with dependencies.

4. How does DVC handle large files?
   DVC stores large files in remote storage (S3, GCP, Azure, etc.) and tracks them with metadata.

5. What is experiment tracking in DVC?
   It logs and compares metrics, parameters, and outputs for multiple runs.

6. What are remotes in DVC?
   Remote storage backends (S3, GCP, SSH, etc.) for storing datasets and models.

7. How does DVC help with reproducibility?
   By ensuring data, code, and models are versioned together, making workflows repeatable.

8. Can DVC be used with Jupyter Notebooks?
   Yes, DVC integrates with notebooks to track data and experiments.

9. How does DVC fit into MLOps?
   It enables version control, pipelines, and experiment tracking, key for production ML.

10. Where is DVC widely used?
    In data science, ML research, computer vision, NLP, and enterprise AI teams.