colls david (15 risultati)

Softcover. xvi, 581 pp. History Workshop Series. Softcover. Good condition; on covers: creasing on spine, light soiling and touches of wear on edges; lightly faded pages.

Paperback. Condizione: new. New Copy. Customer Service Guaranteed.

Condizione: New.

Condizione: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,750grams, ISBN:9780754606505.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: New. Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. 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 from Thoughtworks show you how to apply time-tested software engineering skills and Lean delivery practices that will improve your effectiveness in ML projects.Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help teams avoid common traps in the ML world, so you can iterate more quickly and reliably. With these techniques, data scientists and ML engineers can overcome friction and experience flow when delivering machine learning solutions.This book shows you how to:Apply engineering practices such as writing automated tests, containerizing development environments, and refactoring problematic code basesApply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutionsDesign maintainable and evolvable ML solutions that allow you to respond to changes in an agile fashionApply delivery and product practices to iteratively improve your odds of building the right product for your usersUse intelligent code editor features to code more effectively.

Paperback. Condizione: New. Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. 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 from Thoughtworks show you how to apply time-tested software engineering skills and Lean delivery practices that will improve your effectiveness in ML projects.Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help teams avoid common traps in the ML world, so you can iterate more quickly and reliably. With these techniques, data scientists and ML engineers can overcome friction and experience flow when delivering machine learning solutions.This book shows you how to:Apply engineering practices such as writing automated tests, containerizing development environments, and refactoring problematic code basesApply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutionsDesign maintainable and evolvable ML solutions that allow you to respond to changes in an agile fashionApply delivery and product practices to iteratively improve your odds of building the right product for your usersUse intelligent code editor features to code more effectively.

Condizione: New.

Condizione: New. In.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New. 1st edition NO-PA16APR2015-KAP.

Condizione: New.

Paperback. Condizione: New. Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. 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 from Thoughtworks show you how to apply time-tested software engineering skills and Lean delivery practices that will improve your effectiveness in ML projects.Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help teams avoid common traps in the ML world, so you can iterate more quickly and reliably. With these techniques, data scientists and ML engineers can overcome friction and experience flow when delivering machine learning solutions.This book shows you how to:Apply engineering practices such as writing automated tests, containerizing development environments, and refactoring problematic code basesApply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutionsDesign maintainable and evolvable ML solutions that allow you to respond to changes in an agile fashionApply delivery and product practices to iteratively improve your odds of building the right product for your usersUse intelligent code editor features to code more effectively.

Paperback. Condizione: New. Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. 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 from Thoughtworks show you how to apply time-tested software engineering skills and Lean delivery practices that will improve your effectiveness in ML projects.Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help teams avoid common traps in the ML world, so you can iterate more quickly and reliably. With these techniques, data scientists and ML engineers can overcome friction and experience flow when delivering machine learning solutions.This book shows you how to:Apply engineering practices such as writing automated tests, containerizing development environments, and refactoring problematic code basesApply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutionsDesign maintainable and evolvable ML solutions that allow you to respond to changes in an agile fashionApply delivery and product practices to iteratively improve your odds of building the right product for your usersUse intelligent code editor features to code more effectively.