Structuring Your Machine Learning Project with MLOps in Mind
<p>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 <a href="https://medium.com/towards-data-science/a-beginner-friendly-introduction-to-mlops-95282f25325c" rel="noopener">beginner-friendly tutorial</a> to get up to speed. So let’s dive in and take your ML projects to the next level!</p>
<p><strong>Table of contents:</strong></p>
<p>· <a href="https://towardsdatascience.com/structuring-your-machine-learning-project-with-mlops-in-mind-41a8d65987c9#c6d8" rel="noopener ugc nofollow">1. Introduction</a><br />
· <a href="https://towardsdatascience.com/structuring-your-machine-learning-project-with-mlops-in-mind-41a8d65987c9#6150" rel="noopener ugc nofollow">2. MLOps</a><br />
∘ <a href="https://towardsdatascience.com/structuring-your-machine-learning-project-with-mlops-in-mind-41a8d65987c9#2b4f" rel="noopener ugc nofollow">2.1. Business problem</a><br />
∘ <a href="https://towardsdatascience.com/structuring-your-machine-learning-project-with-mlops-in-mind-41a8d65987c9#aadf" rel="noopener ugc nofollow">2.2. Data engineering</a><br />
∘ <a href="https://towardsdatascience.com/structuring-your-machine-learning-project-with-mlops-in-mind-41a8d65987c9#8845" rel="noopener ugc nofollow">2.3. Machine learning model engineering</a><br />
∘ <a href="https://towardsdatascience.com/structuring-your-machine-learning-project-with-mlops-in-mind-41a8d65987c9#8a22" rel="noopener ugc nofollow">2.4. Code engineering</a><br />
· <a href="https://towardsdatascience.com/structuring-your-machine-learning-project-with-mlops-in-mind-41a8d65987c9#5c87" rel="noopener ugc nofollow">3. Project structure</a><br />
∘ <a href="https://towardsdatascience.com/structuring-your-machine-learning-project-with-mlops-in-mind-41a8d65987c9#477b" rel="noopener ugc nofollow">3.1. Cookiecutter Data Science</a><br />
· <a href="https://towardsdatascience.com/structuring-your-machine-learning-project-with-mlops-in-mind-41a8d65987c9#ca36" rel="noopener ugc nofollow">4. MLOps project structure</a><br />
∘ <a href="https://towardsdatascience.com/structuring-your-machine-learning-project-with-mlops-in-mind-41a8d65987c9#db66" rel="noopener ugc nofollow">4.1. Starting a new MLOps project</a><br />
∘ <a href="https://towardsdatascience.com/structuring-your-machine-learning-project-with-mlops-in-mind-41a8d65987c9#4e0e" rel="noopener ugc nofollow">4.2. Using MLOps project template for handwritten digits classification</a><br />
∘ <a href="https://towardsdatascience.com/structuring-your-machine-learning-project-with-mlops-in-mind-41a8d65987c9#85d1" rel="noopener ugc nofollow">4.3. How to run your project?</a><br />
· <a href="https://towardsdatascience.com/structuring-your-machine-learning-project-with-mlops-in-mind-41a8d65987c9#822e" rel="noopener ugc nofollow">5. Conclusion</a></p>
<p><strong>My MLOps tutorials:</strong></p>
<ul>
<li><a href="https://towardsdatascience.com/a-beginner-friendly-introduction-to-mlops-95282f25325c#aabc" rel="noopener" target="_blank">Tutorial 1: A Beginner-Friendly Introduction to MLOps</a></li>
<li><a href="https://medium.com/towards-data-science/structuring-your-machine-learning-project-with-mlops-in-mind-41a8d65987c9" rel="noopener">Tutorial 2: Structuring Your Machine Learning Project with MLOps in Mind</a></li>
</ul>
<p><em>[I will be updating this list as I publish articles on the subject]</em></p>
<h1>1. Introduction</h1>
<p>In the previous tutorial, we defined MLOps as a way to design, build, and deploy machine learning models in an efficient, optimized, and organized manner. This is achieved by combining a set of techniques, practices, and tools that are often discussed within the context of the MLOps lifecycle.</p>
<p>In the MLOps lifecycle, the first step after understanding the problem is to structure your project. This is typically done by using a template, whether it’s a company template, a public template, or your own template, as we will see later in this tutorial.</p>
<p><a href="https://towardsdatascience.com/structuring-your-machine-learning-project-with-mlops-in-mind-41a8d65987c9">Website</a></p>