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Optimize and Benchmark AI Algorithms for Speed

This course is part of Train, Tune, & Ship: End-to-End Machine Learning Engineering Specialization

Instructor: ansrsource instructors

Included with

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1 module

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

1 module

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

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Build your subject-matter expertise

This course is part of the Train, Tune, & Ship: End-to-End Machine Learning Engineering Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There is 1 module in 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.