If you’re looking to elevate your MLOps projects to the next level, understanding its principles is an essential part of the process. In this article, we’ll offer an introduction to MLOps principles and elucidate the key concepts in an accessible manner. Each principle will receive a dedicated tutorial with practical examples in forthcoming articles. You can access all the examples on my Github profile. However, if you’re new to MLOps, I recommend starting with my beginner-friendly tutorial to get up to speed. So let’s dive in!
Table of contents:
· 1. Introduction
· 2. MLOps principles
· 3. Versioning
· 4. Testing
· 5. Automation
· 6. Monitoring and tracking
· 7. Reproducibility
· 8. Conclusion
My MLOps tutorials:
[I will be updating this list as I publish articles on the subject]
In a previous article, we defined MLOps as a set of techniques and practices used to design, build, and deploy machine learning models in an efficient, optimized, and organized manner. One of the key steps in MLOps is to establish a workflow and maintain it over time.
The MLOps workflow outlines the steps to follow in order to develop, deploy, and maintain machine learning models. It includes the business problem that describes the problem in a structured way, data engineering that involves all the data preparation and preprocessing, machine learning model engineering that involves all the model processing from designing the model to its evaluation, and code engineering that involves serving the model. You can refer to the previous tutorial if you want more details.
