Summary
In this chapter, we explored the key requirements and best practices for building an enterprise ML platform. We discussed how to design a platform that supports the end-to-end ML lifecycle, process automation, and separation of environments. Architectural patterns were reviewed, including how to leverage AWS services to build a robust ML platform on the cloud.
The core capabilities of different ML environments were covered, such as training, hosting, and shared services. Best practices around platform design, operations, governance, and integration were also discussed. You should now have a solid understanding of what an enterprise-grade ML platform entails and key considerations for building one on AWS leveraging proven patterns.
In the next chapter, we will dive deeper into advanced ML engineering topics. This includes distributed training techniques to scale model development and low-latency serving methods for optimizing inference.
