Lilly Tech Systems
Learn Microservices for AI
Design and deploy AI systems using microservice architecture — from model serving and service mesh configuration to monitoring, scaling, and managing distributed AI workloads in production.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why microservices for AI? Monolith vs microservices trade-offs and when to decompose ML systems.
2. Architecture
Design patterns for AI microservices: service boundaries, communication protocols, and data flow.
3. Model Serving
Deploy ML models as microservices using TFServing, Triton, BentoML, and custom serving frameworks.
4. Service Mesh
Manage AI microservice traffic with Istio, Linkerd, and Envoy for routing, load balancing, and canary deployments.
5. Monitoring
Observe distributed AI systems: distributed tracing, metrics, logging, alerting, and model performance tracking.
6. Best Practices
Production patterns for resilience, scaling, testing, CI/CD, and managing complexity in AI microservices.
What You'll Learn
By the end of this course, you'll be able to:
Design AI Architectures
Decompose ML systems into well-bounded microservices with clear contracts and efficient communication.
Serve Models at Scale
Deploy and manage ML models using production-grade serving frameworks on Kubernetes.
Implement Service Mesh
Configure traffic management, security, and observability for AI microservices with Istio or Linkerd.
Monitor and Debug
Build comprehensive observability for distributed AI systems with tracing, metrics, and alerting.