Lilly Tech Systems
Microservices for Enterprise AI
Design and build microservice architectures optimized for AI workloads. Learn service decomposition for ML systems, model serving patterns, orchestration strategies, and production monitoring for AI-powered microservices at enterprise scale.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why microservices for AI? Comparing monolithic vs. microservice ML systems, benefits, challenges, and when to adopt this pattern.
2. Service Design
Decomposing AI systems into services, defining boundaries, API contracts, data ownership, and dependency management.
3. Model Serving
Packaging models as services, gRPC vs REST APIs, model versioning, scaling inference, and GPU resource management.
4. Orchestration
Service mesh, API gateways, workflow orchestration, saga patterns, and managing complex AI inference pipelines.
5. Monitoring
Distributed tracing, model performance tracking, SLO management, alerting strategies, and debugging ML microservices.
6. Best Practices
Production patterns, testing strategies, CI/CD for ML services, resilience engineering, and organizational alignment.
What You'll Learn
By the end of this course, you'll be able to:
Design AI Microservices
Decompose monolithic ML systems into well-bounded microservices with clear APIs and independent deployment capabilities.
Serve Models at Scale
Build production model serving services with proper versioning, scaling, and GPU resource management strategies.
Orchestrate AI Pipelines
Manage complex multi-model inference workflows using service mesh, saga patterns, and intelligent routing.
Monitor and Debug
Implement comprehensive observability for distributed AI services with tracing, metrics, and automated alerting.