Lilly Tech Systems
Cloud AI Design Patterns
Proven design patterns for building production AI systems in the cloud. Learn training patterns for efficient model development, serving patterns for reliable inference, data patterns for ML pipelines, and scaling patterns for growing from prototype to planet-scale AI.
What You'll Learn
Battle-tested patterns for every phase of the AI lifecycle.
Training Patterns
Distributed training, hyperparameter tuning, curriculum learning, and experiment management patterns.
Serving Patterns
Real-time, batch, ensemble, and cascade inference patterns for production deployment.
Data Patterns
Feature engineering, data versioning, streaming, and quality assurance patterns for ML pipelines.
Scaling Patterns
Horizontal scaling, auto-scaling, multi-region, and graceful degradation patterns.
Course Lessons
Follow the lessons to build a comprehensive pattern library for AI systems.
1. Introduction
What are AI design patterns, why they matter, and how to apply them to cloud AI systems.
2. Training Patterns
Distributed training, checkpointing, hyperparameter search, and experiment tracking patterns.
3. Serving Patterns
Synchronous, asynchronous, ensemble, cascade, and stateful serving patterns.
4. Data Patterns
Feature store, data windowing, label management, and data validation patterns.
5. Scaling Patterns
Auto-scaling, multi-region, traffic shaping, and graceful degradation patterns.
6. Best Practices
Pattern selection framework, anti-patterns to avoid, and pattern composition strategies.
Prerequisites
What you need before starting this course.
- Experience deploying applications in the cloud
- Understanding of ML training and inference workflows
- Familiarity with containers, Kubernetes, and microservices
- Basic knowledge of distributed systems concepts