Optimize and Benchmark AI Algorithms for Speed
About this course
In this course, you’ll learn how to analyze and benchmark AI-related algorithms so your systems run efficiently at scale. You’ll use computational complexity and data-structure behavior to predict performance as workloads grow, then validate those predictions with small prototype implementations. You’ll learn how to design fair benchmarks, interpret results using metrics like latency, throughput, memory, and scaling curves, and make defensible decisions when trade-offs are unavoidable. By the end, you’ll be able to identify bottlenecks, communicate performance findings clearly, and choose the best-performing approach for real-world AI workloads using reproducible measurement.
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What you'll learn
- analyze and benchmark AI algorithms
- predict performance using computational complexity
- design fair benchmarks
- interpret performance metrics like latency and throughput
- identify bottlenecks in AI workloads
Course objectives
- understand computational complexity and data structures
- validate performance predictions with prototype implementations
- communicate performance findings effectively
- choose optimal approaches for AI workloads
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