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Scalable AI Microservices: Designing a Robust Node.js Backend for Data Science Operations
Paperback
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ISBN13: 9798171851576
Publisher: Independently Published
Published: Sep 4 2026
Pages: 224
Weight: 0.87
Height: 0.47 Width: 7.00 Depth: 10.00
Language: English
Publisher: Independently Published
Published: Sep 4 2026
Pages: 224
Weight: 0.87
Height: 0.47 Width: 7.00 Depth: 10.00
Language: English
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 infrastructure that scales with your models, your data, and your ambitions.
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
Design AI infrastructure that scales with your models, your data, and your ambitions.
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