Summary
In this chapter, we have learned the key principles of deploying ML models in production. We explored the various deployment methods and targets and their needs. For a comprehensive understanding and hands-on experience, we implemented the deployment to learn how ML models are deployed on a diverse range of deployment targets such as virtual machines, containers, and in an auto-scaling cluster. With this, you are ready to handle any type of deployment challenge that comes your way.
In the next chapter, we will delve into the secrets to building, deploying, and maintaining robust ML services enabled by CI and CD. This will enable the potential of MLOps! Let's delve into it.
