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Managing Machine Learning Projects: From design to deployment
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,
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
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 Delivering machine learning projects is hard; let’s do it better
2 From opportunity to requirements
3 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)
In Managing Machine Learning Projects you’ll learn essential machine learning project management techniques,
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
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 Delivering machine learning projects is hard; let’s do it better
2 From opportunity to requirements
3 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)
272 pages, Paperback
Published July 11, 2023
3 people are currently reading
28 people want to read
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Displaying 1 - 2 of 2 reviews
July 26, 2023
Throughout the book, we follow a hypothetical ML project, a journey from the initial exploration through planning & negotiation to final delivery. I particularly liked this aspect of the book, as the project sounds rather realistic, and demonstrates many obstacles to be found in the real life.
The title should additionally not confuse as "this is just for managers" -- the focus is on project aspects as well as technical hurdles coming from integrating ML, and thus apply to all ML practitioners beyond junior level. People management itself is not a central theme.
The title should additionally not confuse as "this is just for managers" -- the focus is on project aspects as well as technical hurdles coming from integrating ML, and thus apply to all ML practitioners beyond junior level. People management itself is not a central theme.
August 18, 2023
The way the author covers such a complex subject like managing AI/ML projects is awesome. It's exhaustive as well as easy to read. The book introduces complexity without confusion. It can be overwhelming to think on all of it when a project leader is starting an AI/ML project, so I encourage every project leader to tackle down the problem as shown in this text.
Displaying 1 - 2 of 2 reviews