The Most Fundamental Layer of MLOps -- Required Infrastructure

In my previous post, I have discussed the three key components to build an end-to-end MLOps solution, which are data and feature engineering pipelines, ML model training, and retraining pipeline ML model serving pipelines. You can find the article here: Learn the core of MLOPS -- Building ML Pipelines. At the end of my last post, I briefly talked about the fact that the complexities of MLOps solutions can vary significantly from one to another, depending on the nature of the ML project, and more importantly, variations of the underlying infrastructure required. Therefore in today's post, I will explain how the different levels of Infrastructure required, determine the complexities of MLOps solutions, as well as categorize MLOPS solutions into different levels. More importantly, in my view, categorizing MLOps into different levels makes it easier for organizations of any size to adopt MLOps.