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:
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Simon Thompson has spent 30 years developing AI systems. He led the AI research program at BT Labs in the UK, where he helped pioneer Big Data technology in 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.
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 from traditional software. Never fear! This book lays out the unique practices you'll need to ensure your projects succeed.
I can't pin down a moment, or weave a convincing anecdote that explains how I came to realize that writing a book about how to manage a machine learning project would be a good thing to do. The gist of it is that sometime in 2019 I realized that I was talking to a lot of people who had started an ML project and were in trouble with it, and usually I knew why.
There wasn't one common malady or even a single theme, rather failures seemed to come from lots of different directions. Disparate as the failings of these projects were there was a common cause at work here. The folks leading these projects were talented, clever, articulate and skilled, but they were inexperienced.
I was very lucky in the timing of my career. I got into ML when it was on the edge of applications. In the late 1990's ML was out there in the wild, and we could do real things with our three-layer perceptron's and decision trees. It was much harder to deliver though, algorithms needed to be coded by hand, data was vanishing rare and everything ran sooooo slowly. Most of all, ML skills were as rare as the projects that needed them. Most of all, applied ML was seen as R&D. For me this meant that I had the opportunity to develop and work on project after project. Most of them failed - but the ones that did come off really, really, really came off.
At the time the rare wins kept me in work, and kept my career going. In turn this paid the mortgage and filled the freezer. With hindsight I can say now that it was the failures that were the most valuable. I had the luxury of failure and learning, which isn't often afforded to people today. I also got the opportunity to join communities of people going through the same thing, and we would all get really drunk and tell each other sad (and funny) stories of catastrophe. A bunch of practices and behaviours became common knowledge in the clique of AI researchers working in big western companies in those days. I had the luck of being able to pick this all up and then use it.
Having had the luck of getting enough experience to steer an ML project or two to success it would be churlish not to share it. I firmly believe that ML and AI are technologies that can be used for good, hopefully helping people confronted with climate change, pandemics and economic woes. Maybe by sharing knowledge about how to manage ML projects I can help someone else do a couple of projects that make the world a better place!
Two events pushed the book from an idea into the real world though. First Andy Rossiter who was my boss at the time told me we needed to have a methodology to tell customers how we would tackle their problems. I realized that I couldn't really point at one so I'd have to write one. That probably wouldn't have gone all that far if it wasn't for the second event - the pandemic - that meant that I stopped spending hours travelling about and started to have some time in the evening to commit to writing something.
So, here it is. Thank you for buying it. I hope you find it useful and most of all I hope you will share any ideas or thoughts you have for how it should be improved so that we can do better next time.
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Hardcover. Condition: new. Hardcover. The go-to guide in machine learning projects from design to production. No ML skills required! In Managing Machine Learning Projects, you will learn essential machine learning project management techniques, including: Understanding an ML project's requirementsSetting up the infrastructure for the project and resourcing a teamWorking with clients and other stakeholdersDealing with data resources and bringing them into the project for useHandling the lifecycle of models in the projectManaging the application of ML algorithmsEvaluating the performance of algorithms and modelsMaking decisions about which models to adopt for deliveryTaking models through development and testingIntegrating models with production systems to create effective applicationsSteps and behaviours for managing the ethical implications of ML technology About the technology Companies of all shapes, sizes, and industries are investing in machine learning (ML). Unfortunately, around 85% of all ML projects fail. Managing machine learning projects requires adopting a different approach than you would take with standard software projects. You need to account for large and diverse data resources, evaluate and track multiple separate models, and handle the unforeseeable risk of poor performance. Never fear this book lays out the unique practices you will need to ensure your projects succeed! 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781633439023
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Hardback. Condition: New. The go-to guide in machine learning projects from design to production. No ML skills required! In Managing Machine Learning Projects, you will learn essential machine learning project management techniques, including: Understanding an ML project's requirementsSetting up the infrastructure for the project and resourcing a teamWorking with clients and other stakeholdersDealing with data resources and bringing them into the project for useHandling the lifecycle of models in the projectManaging the application of ML algorithmsEvaluating the performance of algorithms and modelsMaking decisions about which models to adopt for deliveryTaking models through development and testingIntegrating models with production systems to create effective applicationsSteps and behaviours for managing the ethical implications of ML technology About the technology Companies of all shapes, sizes, and industries are investing in machine learning (ML). Unfortunately, around 85% of all ML projects fail. Managing machine learning projects requires adopting a different approach than you would take with standard software projects. You need to account for large and diverse data resources, evaluate and track multiple separate models, and handle the unforeseeable risk of poor performance. Never fear - this book lays out the unique practices you will need to ensure your projects succeed! Seller Inventory # LU-9781633439023
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