Want to take your machine learning projects to the next level? In this half-day hands-on training, you’ll learn how to manage ML workflows on HPC systems using MLOps frameworks such as DVC.
Machine learning is becoming increasingly important in many fields, and topics such as reproducibility, data management, and data visualization are essential for any machine learning project. This training will teach you how to use MLOps frameworks to manage your machine learning projects, how to handle data and workflows.
Target group & Prior knowledge
- This training is for you if you need to manage machine learning workflows on HPC systems.
- You will need experience running machine learning workloads in Python or R. You will also need to be comfortable on the command line and have some experience using the git version control system.
Topics covered:
- introduction and motivation
- setting up a git repository for ML
- versioning data using DVC
- defining a workflow with DVC
- comparing experiments using git & DVC
- tracking experiments using DVC Live
- wrap up
When you complete this training you will:
- be able to use DVC to version your data;
- be able to define pipelines with DVC;
- be able to use DVC to manage your machine learning projects;
- be able to reproduce your machine learning experiments.