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.

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)

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