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Introduction to Metaflow Tutorial

In this tutorial, you will learn how to write scalable, production-ready data science and machine learning code. By following along, you will implement a variety of patterns to help you build a machine learning stack to handle data, access compute, facilitate robust versioning, and more. At the end of this tutorial you will be able to:

  • Design basic machine learning workflows.
  • Version and track data in your machine learning systems.
  • Train and track models in parallel.

Prerequisites

We assume you are familiar with the fundamentals of the Python programming language (e.g., loops, variables, importing modules). The interactive aspect of the tutorial involves running Metaflow code in Python scripts and Jupyter Notebooks.

Tutorial Structure

The content includes the following:

Each episode contains either a Metaflow script or a Jupyter notebook to run. You do not need access to cloud computing or a Metaflow deployment to complete the episodes. The estimated time to complete all episodes is 1-2 hours.

Why Metaflow?

The main benefit of using a data science workflow solution like Metaflow when prototyping is that your code will be built on a strong foundation for deploying to a production environment. Metaflow is most useful when projects have scaling requirements, are mission-critical, and/or have many interacting parts. You can read more at these links: