Coursera

Optimize and Benchmark AI Algorithms for Speed

Coursera

Optimize and Benchmark AI Algorithms for Speed

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

Add to your LinkedIn profile

Recently updated!

March 2026

Assessments

5 assignments¹

AI Graded see disclaimer
Taught in English

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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.
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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.

What's included

6 videos3 readings5 assignments

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Instructor

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.