#30 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 3, Lesson 6] | Summary and Q&A

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April 20, 2022
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#30 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 3, Lesson 6]

TL;DR

Measuring human level performance is useful for establishing a baseline and estimating error, but it has limitations and should be used cautiously.

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Key Insights

  • ๐Ÿ˜ซ Measuring human level performance helps set a baseline and estimate error, aiding in error analysis and target setting.
  • ๐ŸŽš๏ธ Using human level performance as a benchmark in academia can help establish the significance of a learning algorithm.
  • ๐ŸŽš๏ธ Measuring human level performance can be problematic when labeling instructions are inconsistent, giving an unfair advantage to learning algorithms.
  • ๐Ÿคจ Raising human level performance through improved label consistency can lead to better learning algorithm performance.
  • ๐ŸŽฎ Measuring human level performance should not be the sole metric for evaluating the superiority of machine learning systems over humans.
  • โœ‹ Real-world applications require more than just high average accuracy, making other arguments more effective in promoting the adoption of machine learning systems.
  • ๐Ÿ› Measuring human level performance should be done cautiously, considering the limitations and potential biases in the ground truth labels.

Transcript

some machine learning tasks are trying to predict an inherently ambiguous output and human level performance can establish a useful baseline of performance as a reference but human level performance is also sometimes misused let's take a look one of the most important uses of measuring human level performance or hrp is to estimate bayes error or ir... Read More

Questions & Answers

Q: How is human level performance useful for error analysis and target setting?

Measuring human level performance helps establish a baseline and estimate the irreducible error, allowing for error analysis and setting realistic targets for machine learning systems.

Q: In what situations is measuring human level performance not effective?

Measuring human level performance can be problematic when labeling instructions are inconsistent, giving an unfair advantage to learning algorithms. It can also mask the fact that a machine learning system performs worse than humans in certain contexts.

Q: Why is measuring human level performance often used in academia?

Demonstrating that a learning algorithm outperforms human level performance helps establish the academic significance of the work and aids in getting research papers published.

Q: Should measuring human level performance be used to prove the superiority of machine learning systems over humans?

Using human level performance as proof of superiority is often ineffective and misleading. Machine learning systems should be evaluated based on their ability to perform specific tasks and meet the needs of the application.

Summary & Key Takeaways

  • Measuring human level performance helps establish a baseline for machine learning tasks and estimate Bayes error.

  • Human level performance is often used as a benchmark in academia to demonstrate the significance of a learning algorithm.

  • Measuring human level performance can help set realistic targets and prioritize error analysis, but it should not be used to prove the superiority of machine learning systems over humans.

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