Isbn: 9781484251065 - agile machine learning: effective machine learning inspired by the agile manifesto (24 risultati)

Condizione: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

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Paperback. Condizione: New. Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto.Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment.The authors' approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. What You'll LearnEffectively run a data engineeringteam that is metrics-focused, experiment-focused, and data-focusedMake sound implementation and model exploration decisions based on the data and the metricsKnow the importance of data wallowing: analyzing data in real time in a group settingRecognize the value of always being able to measure your current state objectivelyUnderstand data literacy, a key attribute of a reliable data engineer, from definitions to expectationsWho This Book Is ForAnyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data.…

Paperback. Condizione: New. Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto.Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment.The authors' approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. What You'll LearnEffectively run a data engineeringteam that is metrics-focused, experiment-focused, and data-focusedMake sound implementation and model exploration decisions based on the data and the metricsKnow the importance of data wallowing: analyzing data in real time in a group settingRecognize the value of always being able to measure your current state objectivelyUnderstand data literacy, a key attribute of a reliable data engineer, from definitions to expectationsWho This Book Is ForAnyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data.…

Condizione: New. 2019. First Edition. paperback. . . . . .

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Paperback. Condizione: Brand New. 248 pages. 9.75x7.00x0.75 inches. In Stock.

Condizione: New. In English.

Condizione: New. 2019. First Edition. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Condizione: New. Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto.Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment.The authors' approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. What You'll LearnEffectively run a data engineeringteam that is metrics-focused, experiment-focused, and data-focusedMake sound implementation and model exploration decisions based on the data and the metricsKnow the importance of data wallowing: analyzing data in real time in a group settingRecognize the value of always being able to measure your current state objectivelyUnderstand data literacy, a key attribute of a reliable data engineer, from definitions to expectationsWho This Book Is ForAnyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data.…

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

Taschenbuch. Condizione: Neu. Agile Machine Learning | Effective Machine Learning Inspired by the Agile Manifesto | Eric Carter (u. a.) | Taschenbuch | xvii | Englisch | 2019 | Apress | EAN 9781484251065 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Paperback. Condizione: New. Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto.Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment.The authors' approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. What You'll LearnEffectively run a data engineeringteam that is metrics-focused, experiment-focused, and data-focusedMake sound implementation and model exploration decisions based on the data and the metricsKnow the importance of data wallowing: analyzing data in real time in a group settingRecognize the value of always being able to measure your current state objectivelyUnderstand data literacy, a key attribute of a reliable data engineer, from definitions to expectationsWho This Book Is ForAnyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data.…

Condizione: new. Questo è un articolo print on demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto.Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment.The authors' approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. What You'll LearnEffectively run a data engineeringteam that is metrics-focused, experiment-focused, and data-focusedMake sound implementation and model exploration decisions based on the data and the metricsKnow the importance of data wallowing: analyzing data in real time in a group settingRecognize the value of always being able to measure your current state objectivelyUnderstand data literacy, a key attribute of a reliable data engineer, from definitions to expectationsWho This Book Is ForAnyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data. 268 pp. Englisch.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Intermediate user level|Authors have proven real-world experience with numerous big data projects coordinated across distributed teams for multiple Microsoft marketsTeaches you how to manage projects involving machine learning more effectively in .…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto.Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment.The authors' approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. What You'll LearnEffectively run a data engineeringteam that is metrics-focused, experiment-focused, and data-focusedMake sound implementation and model exploration decisions based on the data and the metricsKnow the importance of data wallowing: analyzing data in real time in a group settingRecognize the value of always being able to measure your current state objectivelyUnderstand data literacy, a key attribute of a reliable data engineer, from definitions to expectationsWho This Book Is ForAnyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data.…

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto.Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment.The authors¿ approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product.What You'll LearnEffectively run a data engineeringteam that is metrics-focused, experiment-focused, and data-focusedMake sound implementation and model exploration decisions based on the data and the metricsKnow the importance of data wallowing: analyzing data in real time in a group settingRecognize the value of always being able to measure your current state objectivelyUnderstand data literacy, a key attribute of a reliable data engineer, from definitions to expectationsWho This Book Is ForAnyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 268 pp. Englisch.…