Summary

This chapter covered critical architectural considerations for developing intelligent applications. You learned about data modeling and how to evolve your model to fulfill use cases, address technical limitations, and consider patterns and anti-patterns. This approach ensures that data is not only useful but also accessible and optimally utilized across various components of your AI/ML system.

Data storage was another key aspect of this chapter, focusing on the selection of appropriate storage technologies based on different data types and the specific needs of the application. It highlighted the importance of accurately estimating storage requirements and other aspects of choosing the right MongoDB Atlas cluster configuration. The fictitious example of the MDN application served as a practical case study, illustrating how to apply these principles in a real-world scenario.

The chapter also explored the flow of data through ingestion, processing, and output to ensure...