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
- Data Scientists
- Machine Learning Engineers
- Data Engineers
- DevOps / MLOps Engineers
