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

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.

About the Author

Jean Voigt
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

Table of Contents

  • 1 Introduction
  • 2 The Machine Learning Manifesto
  • 3 Data Myopia And Other Distractions
    • 3.1 The individual bias
    • 3.2 The corporate stage 
    • 3.3 Enter the machine 
    • 3.4 To the rescue
    • 3.5 Further reading
  • 4 Organizational & Structural Aspects For AI Teams
    • 4.1 United federation of analytics
    • 4.2 Role inflation
    • 4.3 Role layering
    • 4.4 Serving leaders wanted
    • 4.5 Tolerance considered harmful
    • 4.6 Perspective of future work
    • 4.7 The bottom line
    • 4.8 Further reading
  • 5 Addressing Four Key Cross-Functional Conflicts in AI/ML Initiatives
    • 5.1 Data management and quality
    • 5.2 What is CX-98/001?
    • 5.3 The 80% of work
    • 5.4 The friendly lawyer from the next floor
    • 5.5 Conclusion
    • 5.6 Further reading
  • 6 Six Strategic Process Considerations Beyond MLOps
    • 6.1 Do responsibilities, knowledge & procedures conflict with agile principles?
    • 6.2 Don’t do any harm
    • 6.3 Staying out of court
    • 6.4 Exercise routine
    • 6.5 Cell replication
    • 6.6 Looking beyond
    • 6.7 Summary
    • 6.8 Further reading
  • 7 Six Reasons to Spend More Time Thinking About Labels
    • 7.1 Engineered and collected labels
    • 7.2 Noisy and clean labels
    • 7.3 Many and few labels
    • 7.4 Initial and updated labels
    • 7.5 Definitive and approximate labels
    • 7.6 Consistent and inconsistent labels
    • 7.7 Now what?
    • 7.8 Further reading
  • 8 Seven Critical Machine Intelligence Exams & The Hidden Link of MLOps with Product Management
    • 8.1 Science is not engineering… that is OK!
    • 8.2 Test, test, test…. does it work yet?
    • 8.3 Functional testing
    • 8.4 Performance testing
    • 8.5 Label quality sensitivity testing
    • 8.6 Ethical and regulatory testing
    • 8.7 Consistency testing
    • 8.8 Hyperparameter corner cases
    • 8.9 Drift tests
    • 8.10 Summary
    • 8.11 Further reading
  • 9 Five Ideas to Maintain Senior Executive Involvement for Machine Learning
    • 9.1 Break the ice
    • 9.2 Remove the fear factor
    • 9.3 Make it fun
    • 9.4 Be a champion
    • 9.5 Summary
    • 9.6 Further reading
  • 10 The Machine Learning Product Strategy Journey
    • 10.1 Dare to do more!
    • 10.2 Danger: Construction ahead!
    • 10.3 Plot the course
    • 10.4 It’s a bus
    • 10.5 Take your bearing
    • 10.6 Summary
    • 10.7 Further reading
  • Notes

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