Agile Machine Learning: Effective Machine Learning Inspired by the Agile Manifesto

Author:   Eric Carter ,  Matthew Hurst
Publisher:   APress
Edition:   1st ed.
ISBN:  

9781484251065


Pages:   248
Publication Date:   22 August 2019
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Agile Machine Learning: Effective Machine Learning Inspired by the Agile Manifesto


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Overview

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 engineeringteam 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.

Full Product Details

Author:   Eric Carter ,  Matthew Hurst
Publisher:   APress
Imprint:   APress
Edition:   1st ed.
Weight:   0.515kg
ISBN:  

9781484251065


ISBN 10:   1484251067
Pages:   248
Publication Date:   22 August 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Chapter 1: Early DeliveryChapter 2: Changing RequirementsChapter 3: Continuous DeliveryChapter 4: Aligning with the BusinessChapter 5: Motivated IndividualsChapter 6: Effective CommunicationChapter 7: MonitoringChapter 8: Sustainable Development Chapter 9: Technical ExcellenceChapter 10: Simplicity Chapter 11: Self-organizing TeamsChapter 12: Tuning and Adjusting Chapter 13: Conclusion   

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Author Information

Eric Carter has worked as a Partner Group Engineering Manager on the Bing and Cortana teams at Microsoft. In these roles he worked on search features around products and reviews, business listings, email, and calendar. He currently works on the Microsoft Whiteboard product. Matthew Hurst is a Principal Engineering Manager and Applied Scientist currently working in the Machine Teaching group at Microsoft. He has worked in a number of teams in Microsoft including Bing Document Understanding, Local Search and in various innovation teams.

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