Lilly Tech Systems
Microservices for AI
Decompose AI systems into microservices for model serving, feature computation, and scalable ML pipelines.
Course Lessons
Follow these lessons in order for a complete understanding of microservices for ai.
1. AI Microservices Overview
Learn about ai microservices overview in the context of microservices for ai.
2. Service Decomposition for ML
Learn about service decomposition for ml in the context of microservices for ai.
3. Model Serving Services
Learn about model serving services in the context of microservices for ai.
4. Feature Service Architecture
Learn about feature service architecture in the context of microservices for ai.
5. API Gateway for AI
Learn about api gateway for ai in the context of microservices for ai.
6. Service Mesh for ML
Learn about service mesh for ml in the context of microservices for ai.
7. Migration Strategies
Learn about migration strategies in the context of microservices for ai.
What You'll Learn
By the end of this course, you will be able to:
Understand Core Concepts
Gain deep understanding of the principles and patterns that define microservices for ai.
Apply in Practice
Implement real-world solutions using the architectural patterns and code examples from each lesson.
Make Architecture Decisions
Evaluate trade-offs and choose the right approaches for your specific requirements and constraints.
Build Production Systems
Design and implement production-ready AI systems that are reliable, scalable, and maintainable.