Scalable AI Microservices: Designing a Robust Node.js Backend for Data Science Operations

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 From inference and feature engineering to observability and capacity planning, this book connects Node.js backend engineering with production machine learning operations. Design AI infrastructure that scales with your models, your data, and your ambitions.