This season introduces the basics of structuring machine learning (ML) workflows. You will learn how to structure code so it scales, provides robust versioning, and lets you access your data when and where you need it. You will see how to write code that is easy to share and extend to the needs of your ML applications.
You will discover how to use Metaflow to access these features by representing workflows as directed acyclic graphs (DAGs). You don't need to know much about DAGs yet. You will discover a lot about them as you use Metaflow and can read more here: Why Should I Care About DAGs and Workflows in Data Science?.
To Run The Code
Ensure you have followed the setup steps. Then,
cd <YOUR PATH>/tutorials/intro-to-mf/season-1
What You Will Learn
At the end of this season you will be able to:
- Express ML applications as workflows.
- Read, transform, and write data in the computational steps of your machine learning workflows.
- Analyze and visualize the results of machine learning workflows.