Procuring data, requirements, and tools

Implementing successful MLOps depends on certain factors such as procuring appropriate training data, and having high standards, and appropriate requirements, tools, and infrastructure.

In this section, we will delve into these factors that make robust and scalable MLOps.

Data

I used to believe that learning about data meant mastering tools such as Python, SQL, and regression. The tool is only as good as the person and their understanding of the context around it. The context and domain matter, from data cleaning to modeling to interpretation. The best tools in the world won't fix a bad problem definition (or lack of one). Knowing what problem to solve is a very context-driven and business-dependent decision. Once you are aware of the problem and context, it enables you to discern the right training data needed to solve the problem.

Training data is a vital part of ML systems. It plays a vital role in developing ML systems...