#39 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 3, Lesson 15] | Summary and Q&A

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April 20, 2022
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#39 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 3, Lesson 15]

TL;DR

Estimating the value of machine learning projects involves aligning technical metrics with business goals, considering user experience, search result quality, and revenue potential, while also addressing ethical concerns.

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Key Insights

  • 📈 There is often a gap between the metrics optimized by machine learning engineers and the metrics important for the business.
  • 👨‍🔬 Query level accuracy and search result quality are essential for a good user experience in speech recognition and voice search applications.
  • 😤 Bridging the gap between technical and business teams requires compromise and agreement on suitable metrics.
  • 🎚️ Back-of-the-envelope calculations can help relate word level accuracy to metrics like query level accuracy, search result quality, user engagement, and revenue.
  • 🥡 Ethical considerations, such as societal value and fairness, should be taken into account in project evaluation.
  • 🈸 Domain-dependent ethical frameworks exist and should be considered for each industry and application.
  • 🤗 Open debate and discussion within a team can lead to better ethical decisions and project evaluations.

Transcript

how do you estimate the value of machine learning project this is sometimes not easy to estimate but let me share with you a few best practices tick speech recognition let's say you're working on building a more accurate speech recognition system for the purpose of voice search to let people speak to the smartphone app to do web searches it turns o... Read More

Questions & Answers

Q: What are some metrics that machine learning engineers optimize in speech recognition systems?

Machine learning engineers typically optimize word level accuracy, which measures the accuracy of recognizing individual words spoken by users.

Q: Why is query level accuracy important in a business context?

Query level accuracy measures how often all the words in a user's query are recognized correctly. It is important for ensuring a good user experience in voice search applications.

Q: Other than accuracy, what is another metric that affects search result quality?

Search result quality gauges the relevance and usefulness of the search results. It is crucial for increasing user engagement and driving revenue for the business.

Q: How can technical and business teams reach a compromise on metrics?

By stepping outside their comfort zones, the technical team can stretch further in optimizing metrics, while the business team can adjust their expectations to ensure value for the business.

Summary & Key Takeaways

  • Machine learning projects often have a gap between the metrics optimized by the learning algorithm and the metrics important for the business.

  • In speech recognition for voice search, word level accuracy may be optimized, but query level accuracy and search result quality are crucial for user experience.

  • Bridging the gap between technical and business teams requires compromise and identifying metrics that deliver sufficient value for the business.

  • Ethical considerations, such as societal impact and fairness, should also be addressed in project evaluation.

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