Managing Machine Learning Projects - by Simon Thompson (Paperback)

About the Book

For anyone interested in better management of machine learning projects from idea to production. Managing Machine Learning Projects is a comprehensive guide that does not require any technical skills. This edition will help you discover battle-tested data infrastructure techniques and will guide you through bringing a project to a successful conclusion.

Book Synopsis

Guide machine learning projects from design to production with the techniques in this unique 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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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.

Table of Contents

1 Introduction: Delivering machine learning projects is hard; let's do it better
2 Pre-project: From opportunity to requirements
3 Pre-project: From requirements to proposal
4 Getting started
5 Diving into the problem
6 EDA, ethics, and baseline evaluations
7 Making useful models with ML
8 Testing and selection
9 Sprint 3: system building and production
10 Post project (sprint O)

From the Back Cover

Guide machine learning projects with the techniques in this unique project management guide.

Managing Machine Learning Projects is a comprehensive guide to delivering successful machine learning projects from idea to production. The book is laid out as a series of fictionalised sprints that take you from pre-project requirements and proposal development all the way to deployment. You will discover battle-tested techniques for ensuring you have the appropriate data infrastructure, coordinating ML experiments, and measuring model performance. With this book as your guide, you will know how to bring a project to a successful conclusion, and how to use your lessons learned for future projects.

About the reader

This book is for anyone interested in better management of machine learning projects. No technical skills are required!

Review Quotes

"There's a lot of knowledge in this book that most machine learning practitioners usually only discover after several failures & attempts in trying to deliver their ML projects."
Richard Dze

"Gives great insights to the problems and solutions of not only ML Projects but also data analysis and data science projects."
Marvin Schwarze

"The manual on managing ML projects for less experienced managers."
Maxim Volgin

About the Author

Simon Thompson has spent 25 years developing AI systems. He led the AI research program at BT Labs in the UK, where he helped pioneer Big Data technology for the company and managed an applied research practice for nearly a decade. Simon now works delivering Machine Learning Systems for financial services companies in the City of London as the Head of Data Science at GFT Technologies.