Scalable AI Microservices

AI systems rarely fail because the model is wrong. They fail when the infrastructure cannot keep up.

Scalable AI Microservices is a practical guide to designing robust Node.js microservices for data science and machine learning operations. It focuses on the architecture behind scalable AI-not just the models themselves.
Whether you're breaking apart a monolithic AI backend, building dedicated inference services, or preparing distributed ML infrastructure for production, this book explores the patterns and engineering decisions that matter.
Inside, you'll learn how to:

• Design AI microservices around clear service boundaries and domain-driven principles
• Build dedicated model-serving layers with TensorFlow.js and optimized inference workflows
• Architect event-driven AI systems using Apache Kafka, event sourcing, and dead-letter queues
• Create feature-store and real-time data services with Node.js
• Orchestrate sequential, parallel, and ensemble multi-model workflows
• Choose between REST and gRPC while implementing resilient service-to-service communication
• Build DataOps APIs for training triggers, dataset versioning, model drift, A/B testing, and hyperparameter workflows
• Extend AI infrastructure to edge devices and decentralized environments
• Secure distributed AI systems with mTLS, centralized authentication, PII protection, and compliance logging
• Improve production visibility with OpenTelemetry, distributed tracing, centralized logging, and performance tuning