Automate machine learning expents, pipelines and model deployment (D, MLOps) with Data Version Control (DVC)

What is Data Version Control (DVC) tool and how to use it

How to build reproducible Machine Learning expents

How to automate pipelines execution with DVC

How to manage data and model versioning

How to organize code in Machine Learning projects

Basics of how to build, test, deploy and monitor Machine Learning model (D and MLOps)

How to start to use DVC in your projects (step by step)

Python

Basic knowledge in CLI and Git is a plus

Linux / Mac OS

During these lessons you'll discover base features of Data Version Control (DVC), how it works and how it may benefit your Machine Learning and Data Science projects.

During this course listeners learn eeering approaches in ML around a few practical examples. Screencast videos, repositories with examples and templates to put your hands dirty and make it easier apply best features in your own projects.

After this course you will be able to

Use DVC for data and artifacts version control

Build reproducible machine learning pipelines

Manage Machine Learning expents

Automate pipelines configuration

Organize code in Machine Learning projects

Setup D pipelines with GitLab / GitHub and DVC

Data Scientists

Machine Learning Eeers

Data Eeers

DevOps / MLOps Eeers



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