Managing Machine Learning Projects, Video Edition

Managing Machine Learning Projects, Video Edition

by Simon Thompson
Released July 2023
Publisher(s): Manning Publications
ISBN: None

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Video description

In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.

Guide machine learning projects from design to production with the techniques in this one-of-a-kind project management guide. No ML skills required

In Managing Machine Learning Projects you’ll learn essential machine learning project management techniques, including:

  • Understanding an ML project’s requirements
  • Setting up the infrastructure for the project and resourcing a team
  • Working with clients and other stakeholders
  • Dealing with data resources and bringing them into the project for use
  • Handling the lifecycle of models in the project
  • Managing the application of ML algorithms
  • Evaluating the performance of algorithms and models
  • Making decisions about which models to adopt for delivery
  • Taking models through development and testing
  • Integrating models with production systems to create effective applications
  • Steps and behaviors for managing the ethical implications of ML technology

Managing Machine Learning Projects is an end-to-end guide for delivering machine learning applications on time and under budget. It lays out tools, approaches, and processes designed to handle the unique challenges of machine learning project management. You’ll follow an in-depth case study through a series of sprints and see how to put each technique into practice. The book’s strong consideration to data privacy, and community impact ensure your projects are ethical, compliant with global legislation, and avoid being exposed to failure from bias and other issues.

About the Technology
Ferrying machine learning projects to production often feels like navigating uncharted waters. From accounting for large data resources to tracking and evaluating multiple models, machine learning technology has radically different requirements than traditional software. Never fear! This book lays out the unique practices you’ll need to ensure your projects succeed.

About the Book
Managing Machine Learning Projects is an amazing source of battle-tested techniques for effective delivery of real-life machine learning solutions. The book is laid out across a series of sprints that take you from a project proposal all the way to deployment into production. You’ll learn how to plan essential infrastructure, coordinate experimentation, protect sensitive data, and reliably measure model performance. Many ML projects fail to create real value—read this book to make sure your project is a success.

What's Inside
  • Set up infrastructure and resource a team
  • Bring data resources into a project
  • Accurately estimate time and effort
  • Evaluate which models to adopt for delivery
  • Integrate models into effective applications


About the Reader
For anyone interested in better management of machine learning projects. No technical skills required.

About the Author
Simon Thompson has spent 25 years developing AI systems to create applications for use in telecoms, customer service, manufacturing and capital markets. He led the AI research program at BT Labs in the UK, and is now the Head of Data Science at GFT Technologies.

Quotes
Provides many examples of practical implementation issues including scoping, sprints, case studies, and request tickets.
- Abi Aryan, MLOps Podcast

Golden for all managers, even those with a less technical background. Lucid concept explanations.
- Amrita Sarkar, Thomson Reuters

Years of experience boiled down to workable checklists, handy anecdotes, and guidance on regulatory and legal frameworks. Ignore at your peril.
- Dan Gilks, British Telecommunications

Table of contents

  1. Chapter 1. Introduction: Delivering machine learning projects is hard; let’s do it better
  2. Chapter 1. Why is ML important?
  3. Chapter 1. Other machine learning methodologies
  4. Chapter 1. Understanding this book
  5. Chapter 1. Case study: The Bike Shop
  6. Chapter 1. Summary
  7. Chapter 2. Pre-project: From opportunity to requirements
  8. Chapter 2. Project management infrastructure
  9. Chapter 2. Project requirements
  10. Chapter 2. Data
  11. Chapter 2. Security and privacy
  12. Chapter 2. Corporate responsibility, regulation, and ethical considerations
  13. Chapter 2. Development architecture and process
  14. Chapter 2. Summary
  15. Chapter 3. Pre-project: From requirements to proposal
  16. Chapter 3. Create an estimate
  17. Chapter 3. Pre-sales/pre-project administration
  18. Chapter 3. Pre-project/pre-sales checklist
  19. Chapter 3. The Bike Shop pre-sales
  20. Chapter 3. Pre-project postscript
  21. Chapter 3. Summary
  22. Chapter 4. Getting started
  23. Chapter 4. Finalize team design and resourcing
  24. Chapter 4. A way of working
  25. Chapter 4. Infrastructure plan
  26. Chapter 4. The data story
  27. Chapter 4. Privacy, security, and an ethics plan
  28. Chapter 4. Project roadmap
  29. Chapter 4. Sprint 0 checklist
  30. Chapter 4. Bike Shop: project setup
  31. Chapter 4. Summary
  32. Chapter 5. Diving into the problem
  33. Chapter 5. Understanding the data
  34. Chapter 5. Business problem refinement, UX, and application design
  35. Chapter 5. Building data pipelines
  36. Chapter 5. Model repository and model versioning
  37. Chapter 5. Summary
  38. Chapter 6. EDA, ethics, and baseline evaluations
  39. Chapter 6. Ethics checkpoint
  40. Chapter 6. Baseline models and performance
  41. Chapter 6. What if there are problems?
  42. Chapter 6. Pre-modeling checklist
  43. Chapter 6. The Bike Shop: Pre-modelling
  44. Chapter 6. Summary
  45. Chapter 7. Making useful models with ML
  46. Chapter 7. Feature engineering and data augmentation
  47. Chapter 7. Model design
  48. Chapter 7. Making models with ML
  49. Chapter 7. Stinky, dirty, no good, smelly models
  50. Chapter 7. Summary
  51. Chapter 8. Testing and selection
  52. Chapter 8. Testing processes
  53. Chapter 8. Model selection
  54. Chapter 8. Post modelling checklist
  55. Chapter 8. The Bike Shop: sprint 2
  56. Chapter 8. Summary
  57. Chapter 9. Sprint 3: system building and production
  58. Chapter 9. Types of ML implementations
  59. Chapter 9. Nonfunctional review
  60. Chapter 9. Implementing the production system
  61. Chapter 9. Logging, monitoring, management, feedback, and documentation
  62. Chapter 9. Pre-release testing
  63. Chapter 9. Ethics review
  64. Chapter 9. Promotion to production
  65. Chapter 9. You aren’t done yet
  66. Chapter 9. The Bike Shop sprint 3
  67. Chapter 9. Summary
  68. Chapter 10. Post project (sprint Ω)
  69. Chapter 10. Off your hands and into production?
  70. Chapter 10. Team post-project review
  71. Chapter 10. Improving practice
  72. Chapter 10. New technology adoption
  73. Chapter 10. Case study
  74. Chapter 10. Goodbye and good luck
  75. Chapter 10. Summary

Product information

  • Title: Managing Machine Learning Projects, Video Edition
  • Author(s): Simon Thompson
  • Release date: July 2023
  • Publisher(s): Manning Publications
  • ISBN: None