Lilly Tech Systems
ML Platform Architecture
Design internal ML platforms with experiment tracking, model registry, compute orchestration, and multi-tenancy.
Course Lessons
Follow these lessons in order for a complete understanding of ml platform architecture.
1. ML Platform Overview
Learn about ml platform overview in the context of ml platform architecture.
2. Platform Component Design
Learn about platform component design in the context of ml platform architecture.
3. Experiment Tracking Architecture
Learn about experiment tracking architecture in the context of ml platform architecture.
4. Model Registry Design
Learn about model registry design in the context of ml platform architecture.
5. Compute Orchestration
Learn about compute orchestration in the context of ml platform architecture.
6. Multi-Tenancy for ML
Learn about multi-tenancy for ml in the context of ml platform architecture.
7. Platform Maturity Model
Learn about platform maturity model in the context of ml platform architecture.
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 ml platform architecture.
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.