AI/ML Application Design
As the landscape of intelligent applications evolves, their architectural design becomes pivotal for efficiency, scalability, operability, and security. This chapter provides a guide on key topics to consider as you embark on creating robust and responsive AI/ML applications.
The chapter begins with data modeling, examining how to organize data in a way that maximizes effectiveness for three different consumers: humans, applications, and AI models. You will learn about data storage, considering the impact of different data types and determining the best storage technology. You will estimate storage needs and determine the best MongoDB Atlas cluster configuration for your example application.
As you learn about data flow, you will explore the detailed movement of data through ingestion, processing, and output to maintain integrity and velocity. This chapter also addresses data lifecycle management, including updates, aging, and retention, ensuring that...
