Screen LibraryAgile machine learning : effective machine learning inspired by the agile manifesto /
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 e...
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| Main Authors: | |
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| Group Author: | |
| Published: |
Apress,
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| Publisher Address: | [Berkeley, CA] : |
| Publication Dates: | [2019] |
| Literature type: | Book |
| Language: | English |
| Subjects: | |
| 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. |
| Carrier Form: | xvii, 248 pages : illustrations ; 25 cm |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9781484251065 1484251067 |
| Index Number: | Q325 |
| CLC: | TP181 |
| Call Number: | TP181/C323 |