Designing Scalable AI Systems

This comprehensive guide explores the principles and practices for designing scalable AI-powered applications. Dive into core concepts like AI pipelines, orchestration, event-driven systems, and distributed AI architectures. Learn how to build robust, high-performance AI solutions using microservices and AI APIs, complete with real-world system design examples.

Chapters

1. 01 Building AI/ML Pipelines: From Data to Deployment 19m
2. 02 Case Study: Architecting a Real-time Recommendation Engine 18m
3. 03 Building Reliable AI with Data Quality and Model Trustworthiness 15m
4. 04 Designing AI APIs: Seamless Integration for Intelligent Services 17m
5. 05 Distributed AI: Scaling Training and Inference Across Resources 19m
6. 06 Build Real-time AI Systems with Event-Driven Architectures 16m
7. 07 Architecting AI Systems with LLMs, Generative AI, and Agents 19m
8. 08 Introduction to AI System Design: Principles & Foundations 14m
9. 09 Microservices for AI: Architecting Modular & Scalable Components 15m
10. 10 Observability for AI Systems: Monitoring, Logging & Tracing 17m
11. 11 Orchestrating Complex AI Workflows and Multi-Agent Systems 17m
12. 12 Designing Secure, Private, and Responsible AI Systems 15m