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Computer Vision with Metaflow - Beginner Tutorial

In this tutorial, you will build a set of workflows to train and evaluate a machine learning model that performs image classification. You will use Keras and Metaflow to write computer vision code you can use as a foundation for real-world data science projects.

Prerequisites

We assume that you have taken the introductory tutorials or know the basics of Metaflow.

Tutorial Structure

The content includes the following:

Each episode contains either a Metaflow script to run and/or a Jupyter notebook. 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:

After completing the lessons, you will be able to transfer insights and code from the tutorial to your real-world data science projects. It is important to be mindful that this is a beginner tutorial so it will not reflect many important challenges to consider in production ML environments. For example, in production, you may consider using Metaflow features such as the @conda decorator for dependency management, @batch or @kubernetes for remote execution, and @schedule to automatically trigger jobs.