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Structuring Your Machine Learning Project with MLOps in Mind
MLOps in Action: Project Structuring
If you’re looking to take your machine learning projects to the next level, MLOps is an essential part of the process. In this article, we’ll provide you with a practical tutorial on how to structure your projects for MLOps, using the classic handwritten digit classification problem as an example. We’ll take you step-by-step through the process of creating a basic project template that you can use to organize your own projects. By the end of this tutorial, you’ll have a solid understanding of MLOps principles and how to apply them to your own projects. However, if you’re new to MLOps, we recommend starting with my beginner-friendly tutorial to get up to speed. So let’s dive in and take your ML projects to the next level!
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Table of contents:
· 1. Introduction
· 2. MLOps
∘ 2.1. Business problem
∘ 2.2. Data engineering
∘ 2.3. Machine learning model engineering
∘ 2.4. Code engineering
· 3. Project structure
∘ 3.1. Cookiecutter Data Science
· 4. MLOps project structure
∘ 4.1. Starting a new MLOps project
∘ 4.2. Using MLOps project template for handwritten digits classification
∘ 4.3. How to run your project?
· 5. Conclusion
