Automate machine learning experiments, pipelines and model deployment (CI/CD, MLOps) with Data Version Control (DVC) What you'll learn:
What is Data Version Control (DVC) tool and how to use it
How to build reproducible Machine Learning experiments
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 (CI/CD and MLOps)
How to start to use DVC in your projects (step by step)

Requirements
Python
Basic knowledge in CLI and Git is a plus
Linux / Mac OS

Description
Online video course to teach basics for Machine Learning experiment management, pipelines automation and CI/CD to deliver ML solution into production. 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 engineering 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 experiments

Automate pipelines configuration

Organize code in Machine Learning projects

Setup CI/CD pipelines with GitLab / GitHub and DVC

Who this course is for
Data Scientists
Machine Learning Engineers
Data Engineers
DevOps / MLOps Engineers

https://www.udemy.com/course/machine-learning-experiments-and-engineering-with-dvc/