Effective Machine Learning Teams (Paperback)
Idioma: inglés
Editorial: O'Reilly Media, Sebastopol, 2024
- Tapa blanda
- Nuevo
Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Vendedor de AbeBooks desde 22 de junio de 2007
Condición: Nuevo
EUR 104,54
Cantidad disponible: 1 disponibles
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Paperback. Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists, ML engineers, and their leaders will learn how to bridge the gap between data science and Lean product delivery in a practical and simple way. David Tan, Ada Leung, and Dave Colls show you how to apply time-tested software engineering skills and Lean product delivery practices to reduce toil and waste, shorten feedback loops, and improve your team's flow when building ML systems and products. Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help your team avoid common traps in the ML world, so you can iterate and scale more quickly and reliably. You'll learn how to overcome friction and experience flow when delivering ML solutions. You'll also learn how to: Write automated tests for ML systems, containerize development environments, and refactor problematic codebases Apply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutions Apply Lean delivery and product practices to improve your odds of building the right product for your users Identify suitable team structures and intra- and inter-team collaboration techniques to enable fast flow, reduce cognitive load, and scale ML within your organization About the AuthorsDavid Tan is a Senior ML Engineer at Thoughtworks. He has worked on multiple data and machine learning projects and applied time-tested software engineering practices to help teams iterate more quickly and reliably in the machine learning development lifecycle. Ada Leung is a Senior Business Analyst at Thoughtworks. She has technology delivery experience across several industries and her experience includes breaking down complex problems in varying domains, including customer facing applications, scaling of ML solutions, and more recently, data strategy and delivery of data platforms. She has been part of exemplar cross-functional delivery teams, both in-person and remotely, and is an advocate of cultivation as a way to build high performing teams. David "Dave" Colls is a technology leader with broad experience helping software and data teams deliver great results. David's technical background is in engineering design, simulation, optimization, and large-scale data-processing software. At Thoughtworks, he has led numerous agile and lean transformation projects, and most recently he established the Data and AI practice in Australia. In his practice leadership role, he develops new ML services, consults on ML strategy, and provides leadership to the delivery of ML initiatives. With this practical guide, data scientists and ML engineers will learn how to bridge the gap between data science and Lean software delivery in a practical and simple way. David Tan and Ada Leung show you how to apply time-tested software engineering skills and Lean delivery practices that will improve your effectiveness in ML projects. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…
N° de ref. del artículo 9781098144630
- Título
- Effective Machine Learning Teams (Paperback)
- Autor
- David Tan
- Editorial
- O'Reilly Media, Sebastopol
- Año de publicación
- 2024
- Estado
- new
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 10
- 1098144635
- ISBN 13
- 9781098144630
Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists, ML engineers, and their leaders will learn how to bridge the gap between data science and Lean product delivery in a practical and simple way. David Tan, Ada Leung, and Dave Colls show you how to apply time-tested software engineering skills and Lean product delivery practices to reduce toil and waste, shorten feedback loops, and improve your team's flow when building ML systems and products.
Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help your team avoid common traps in the ML world, so you can iterate and scale more quickly and reliably. You'll learn how to overcome friction and experience flow when delivering ML solutions.
You'll also learn how to:
- Write automated tests for ML systems, containerize development environments, and refactor problematic codebases
- Apply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutions
- Apply Lean delivery and product practices to improve your odds of building the right product for your users
- Identify suitable team structures and intra- and inter-team collaboration techniques to enable fast flow, reduce cognitive load, and scale ML within your organization
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
Ada Leung is a Senior Business Analyst at Thoughtworks. She has technology delivery experience across several industries and her experience includes breaking down complex problems in varying domains, including customer facing applications, scaling of ML solutions, and more recently, data strategy and delivery of data platforms. She has been part of exemplar cross-functional delivery teams, both in-person and remotely, and is an advocate of cultivation as a way to build high performing teams.
David "Dave" Colls is a technology leader with broad experience helping software and data teams deliver great results. David's technical background is in engineering design, simulation, optimization, and large-scale data-processing software. At Thoughtworks, he has led numerous agile and lean transformation projects, and most recently he established the Data and AI practice in Australia. In his practice leadership role, he develops new ML services, consults on ML strategy, and provides leadership to the delivery of ML initiatives.
“Acerca de” puede pertenecer a otra edición de este título.
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