If you’re looking to take your MLOps projects to the next level, understanding its principles is an essential part of the process. In this article, we will provide an introduction to the principles of MLOps and elucidate the key concepts in an accessible way. Each principle will receive a dedicated tutorial with practical examples in future articles. You can access all the examples in my github profile. However, if you are new to MLOps, I recommend starting with my tutorial for beginners to catch. So let’s dive in!
Table of Contents:
· 1. Introduction
· 2. MLOps Principles
· 3. Versioning
· 4. Tests
· 5. Automation
· 6. Monitoring and tracking
· 7. Reproducibility
· 8. Conclusion
My MLOps tutorials:
(I will update this list as I publish articles on the topic)
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 establishing a workflow and maintaining it over time.
The MLOps Workflow Describes the steps to follow to develop, deploy, and maintain machine learning models. It includes business problem that describes the problem in a structured way, data engineering that involves all data preparation and preprocessing, machine learning model engineering that involves all model processing from model design to model processing. evaluation, and the code engineering that involves serving the client. model. You can refer to the previous tutorial if you want more details.
