Summary

In this chapter, we discussed the key requirements for building an enterprise ML platform to meet needs such as end-to-end ML life cycle support, process automation, and separating different environments. We also talked about architecture patterns and how to build an enterprise ML platform on AWS using AWS services. We discussed the core capabilities of different ML environments, including training, hosting, and shared services. You should now have a good understanding of what an enterprise ML platform could look like, as well as the key considerations for building one using AWS services. You have also developed some hands-on experience in building the components of the MLOps architecture and automating model training and deployment. In the next chapter, we will discuss advanced ML engineering by covering large-scale distributed training and the core concepts for achieving low-latency inference.