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The Machine Learning Manifesto of Cultural Primitives for Engineering Teams in Traditional Organizations

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About

About

About the Book

In this experience-based account, Jean Voigt describes cultural, organizational and critical technical aspects for consideration when building artificial intelligence and machine learning teams. Covering a broad area from, legal, technical data and model management to leadership and people management aspects the book provokes to take a step back and reflect. Each chapter includes a comprehensive reading list to further dive into specific topics in more detail. The extend of recent research material combined with Jean's leadership experience enables executives and engineers to improve their AI and ML initiatives and increase the odds of successful completion.

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Author

About the Author

Jean Voigt

Jean has been building and leading data, artificial intelligence, and machine learning teams for more than ten years. As a CFA Charterholder with a Ph.D. in Computer Science from the University of Zurich, he has built expertise in driving the organizational design and strategic product management aspect of machine intelligence. The Machine Learning Manifesto principles summarize vital aspects Jean has observed in the field and released to the community of machine learning practitioners for endorsement and adaptation.


Jean lives in Zurich, is an avid mountaineer, and regularly spends a few hours with recreational coding.


Blog: https://jeanvoigt.medium.com

Machine Learning Manifesto: https://machinelearningmanifesto.com

Contents

Table of Contents

1Introduction

2The Machine Learning Manifesto

3Data Myopia And Other Distractions

  1. 3.1The individual bias
  2. 3.2The corporate stage 
  3. 3.3Enter the machine 
  4. 3.4To the rescue
  5. 3.5Further reading

4Organizational & Structural Aspects For AI Teams

  1. 4.1United federation of analytics
  2. 4.2Role inflation
  3. 4.3Role layering
  4. 4.4Serving leaders wanted
  5. 4.5Tolerance considered harmful
  6. 4.6Perspective of future work
  7. 4.7The bottom line
  8. 4.8Further reading

5Addressing Four Key Cross-Functional Conflicts in AI/ML Initiatives

  1. 5.1Data management and quality
  2. 5.2What is CX-98/001?
  3. 5.3The 80% of work
  4. 5.4The friendly lawyer from the next floor
  5. 5.5Conclusion
  6. 5.6Further reading

6Six Strategic Process Considerations Beyond MLOps

  1. 6.1Do responsibilities, knowledge & procedures conflict with agile principles?
  2. 6.2Don’t do any harm
  3. 6.3Staying out of court
  4. 6.4Exercise routine
  5. 6.5Cell replication
  6. 6.6Looking beyond
  7. 6.7Summary
  8. 6.8Further reading

7Six Reasons to Spend More Time Thinking About Labels

  1. 7.1Engineered and collected labels
  2. 7.2Noisy and clean labels
  3. 7.3Many and few labels
  4. 7.4Initial and updated labels
  5. 7.5Definitive and approximate labels
  6. 7.6Consistent and inconsistent labels
  7. 7.7Now what?
  8. 7.8Further reading

8Seven Critical Machine Intelligence Exams & The Hidden Link of MLOps with Product Management

  1. 8.1Science is not engineering… that is OK!
  2. 8.2Test, test, test…. does it work yet?
  3. 8.3Functional testing
  4. 8.4Performance testing
  5. 8.5Label quality sensitivity testing
  6. 8.6Ethical and regulatory testing
  7. 8.7Consistency testing
  8. 8.8Hyperparameter corner cases
  9. 8.9Drift tests
  10. 8.10Summary
  11. 8.11Further reading

9Five Ideas to Maintain Senior Executive Involvement for Machine Learning

  1. 9.1Break the ice
  2. 9.2Remove the fear factor
  3. 9.3Make it fun
  4. 9.4Be a champion
  5. 9.5Summary
  6. 9.6Further reading

10The Machine Learning Product Strategy Journey

  1. 10.1Dare to do more!
  2. 10.2Danger: Construction ahead!
  3. 10.3Plot the course
  4. 10.4It’s a bus
  5. 10.5Take your bearing
  6. 10.6Summary
  7. 10.7Further reading

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