Chapter 6: Introducing ML Systems Architecture
In this chapter, you will learn about general principles of Machine Learning (ML) systems architecture in the broader context of Software Engineering (SWE) and common issues with deploying models in production in a reliable way. You will also have the opportunity to follow along with architecting our ML systems. We will briefly look at how with MLflow, in conjunction with other relevant tools, we can build reliable and scalable ML platforms.
Specifically, we will look at the following sections in this chapter:
- Understanding challenges with ML systems and projects
- Surveying state-of-the-art ML platforms
- Architecting the PsyStock ML platform
You will follow a process of understanding the problem, studying different solutions from lead companies in the industry, and then developing your own relevant architecture. This three-step approach is transferrable to any future ML system that you want to develop...
