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⇱ Optimize and Benchmark AI Algorithms for Speed | Coursera

Optimize and Benchmark AI Algorithms for Speed

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Optimize and Benchmark AI Algorithms for Speed

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Intermediate level

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2 hours to complete
Flexible schedule
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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace

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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.

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

6 videos•Total 29 minutes
  • Welcome and Why Speed Matters in Real AI Systems•4 minutes
  • Understanding Complexity: From Big-O to Practical Speed•5 minutes
  • Hidden Costs: Constants, Cache Effects, and Real-World Slowdowns•6 minutes
  • Why Benchmarking Beats Guesswork•5 minutes
  • Building Simple Benchmarks: Tools, Timers, and Fair Tests•6 minutes
  • Congratulations and Continuous Learning Journey•4 minutes
3 readings•Total 30 minutes
  • Data Structures That Scale: Trees, Hash Maps, and Heaps•10 minutes
  • Interpreting Benchmark Data: Throughput, Latency, Memory, and Curves•10 minutes
  • Documenting Benchmarks for Engineering Decisions•10 minutes
5 assignments•Total 65 minutes
  • Hands-On Activity: Complexity Match-Up: Predict the Faster Method•10 minutes
  • Practice Quiz: Test Your Complexity and Data Structure Skills•10 minutes
  • Hands-On Activity: Benchmark Two Approaches and Compare•15 minutes
  • Practice Quiz: Check Your Benchmarking and Performance Insights•10 minutes
  • Graded Quiz: Algorithm Performance and Benchmarking Assessment•20 minutes

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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.