Lilly Tech Systems
Model Performance Testing
Benchmark and profile AI models for latency, throughput, memory usage, and GPU utilization with practical optimization strategies.
Course Lessons
Work through these lessons sequentially or jump to the topic most relevant to you.
1. Performance Metrics Overview
Key performance metrics for AI systems
2. Latency and Throughput Testing
Measuring model inference speed
3. Memory Profiling for Models
Profiling memory usage of ML models
4. GPU Utilization Analysis
Monitoring and optimizing GPU usage
5. Batch vs Real-Time Performance
Testing different inference modes
6. Performance Regression Detection
Detecting performance degradation
7. Optimization Strategies
Strategies for improving model performance
What You'll Learn
By the end of this course, you will be able to:
Core Concepts
Understand the fundamental principles and techniques of model performance testing for production AI systems.
Practical Skills
Build hands-on skills with real code examples, frameworks, and tools used by industry professionals.
Best Practices
Apply industry best practices and avoid common pitfalls when implementing testing in your ML projects.
Production Ready
Ship reliable, well-tested AI systems with confidence using automated testing pipelines.