Agile machine learning : effective machine learning inspired by the agile manifesto / Eric Carter, Matthew Hurst.  (Text) (Text)

Carter, Eric
Hurst, Matthew
Call no.: Q325.5 .C37 2019Publication: Berkeley, CA : Apress, 2019Description: xvi, 248 p. : illNotes: Includes and index.ISBN: 9781484251065; 1484251067Subject(s): Machine learningLOC classification: Q325.5 | .C37 2019
Contents:Chapter 1: Early Delivery -- Chapter 2: Changing Requirements -- Chapter 3: Continuous Delivery -- Chapter 4: Aligning with the Business -- Chapter 5: Motivated Individuals -- Chapter 6: Effective Communication -- Chapter 7: Monitoring -- Chapter 8: Sustainable Development -- Chapter 9: Technical Excellence -- Chapter 10 Simplicity -- Chapter 11: Self-organizing Teams -- Chapter 12: Tuning and Adjusting -- Chapter 13: Conclusion.
Summary: 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 Learn Effectively run a data engineering team that is metrics-focused, experiment-focused, and data-focused Make sound implementation and model exploration decisions based on the data and the metrics Know the importance of data wallowing: analyzing data in real time in a group setting Recognize the value of always being able to measure your current state objectively Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations Who This Book Is For Anyone 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.
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Book Book Professor Sangvian Indaravijaya Library
General Books General Stacks Q325.5 .C37 2019 (เรียกดูชั้นหนังสือ) พร้อมให้บริการ
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Includes and index.

Chapter 1: Early Delivery -- Chapter 2: Changing Requirements -- Chapter 3: Continuous Delivery -- Chapter 4: Aligning with the Business -- Chapter 5: Motivated Individuals -- Chapter 6: Effective Communication -- Chapter 7: Monitoring -- Chapter 8: Sustainable Development -- Chapter 9: Technical Excellence -- Chapter 10 Simplicity -- Chapter 11: Self-organizing Teams -- Chapter 12: Tuning and Adjusting -- Chapter 13: Conclusion.

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 Learn Effectively run a data engineering team that is metrics-focused, experiment-focused, and data-focused Make sound implementation and model exploration decisions based on the data and the metrics Know the importance of data wallowing: analyzing data in real time in a group setting Recognize the value of always being able to measure your current state objectively Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations Who This Book Is For Anyone 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.

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