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|a Q325.5
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|a Thompson, Simon G.
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| 245 |
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|a Managing machine learning projects
|c Simon Thompson
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| 250 |
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|a Video edition
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| 260 |
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|a [Place of publication not identified]
|b Manning Publications
|c 2023
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| 300 |
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|a 1 video file (10 hr., 19 min.)
|b sound, color
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| 653 |
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|a Vidéo en continu
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|a Machine learning / Management
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|a Project management / fast
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|a Apprentissage automatique / Gestion
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| 653 |
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|a Project management / http://id.loc.gov/authorities/subjects/sh85065919
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|a streaming video / aat
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| 653 |
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|a Gestion de projet
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|a Streaming video / http://id.loc.gov/authorities/subjects/sh2005005237
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|a eng
|2 ISO 639-2
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|b OREILLY
|a O'Reilly
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|u https://learning.oreilly.com/videos/~/9781633439023VE/?ar
|x Verlag
|3 Volltext
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|a 658.404
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|a 006.3/1068
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| 520 |
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|a 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.
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| 520 |
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|a 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.
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|a Guide machine learning projects from design to production with the techniques in this one-of-a-kind project management guide.
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| 520 |
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|a 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.
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| 520 |
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|a 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
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