Deployment Patterns and Tools
In this chapter, we will dive into some important concepts around the deployment of your machine learning (ML) solution. We will begin to close the circle of the ML development lifecycle and lay the groundwork for getting your solutions out into the world.
The act of deploying software, of taking it from a demo you can show off to a few stakeholders to a service that will ultimately impact customers or colleagues, is a very exhilarating but often challenging exercise. It also remains one of the most difficult aspects of any ML project and getting it right can ultimately make the difference between generating value or just hype.
We are going to explore some of the main concepts that will help your ML engineering team cross the chasm between a fun proof-of-concept to solutions that can run on scalable infrastructure in an automated way. This will require us to first cover questions of how to design and architect your ML systems, particularly if...