Details

Agile Machine Learning


Agile Machine Learning

Effective Machine Learning Inspired by the Agile Manifesto

von: Eric Carter, Matthew Hurst

CHF 77.00

Verlag: Apress
Format: PDF
Veröffentl.: 21.08.2019
ISBN/EAN: 9781484251072
Sprache: englisch

Dieses eBook enthält ein Wasserzeichen.

Beschreibungen

<p>Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto.</p>

<p>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. <b><i>Agile Machine Learning </i></b>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.</p>

<p>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.</p><p><br></p><p><b> What You'll Learn</b></p>

<p></p><ul><li>Effectively run a data engineeringteam that is metrics-focused, experiment-focused, and data-focused<br></li><li>Make sound implementation and model exploration decisions based on the data and the metrics<br></li><li>Know the importance of data wallowing: analyzing data in real time in a group setting<br></li><li>Recognize the value of always being able to measure your current state objectively<br></li><li>Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations<br></li></ul><p></p>











<p><b><br></b></p><p><b>Who This Book Is For</b></p><p>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.</p>
<p>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.<br></p>
<div><b>Eric Carter </b>has worked as a Partner Group Engineering Manager on the Bing and Cortana&nbsp;teams at Microsoft. In these roles he worked on search features around products and reviews,&nbsp;business listings, email, and calendar. He currently works on the Microsoft Whiteboard product.</div><div><b><br></b></div><div><b>Matthew Hurst </b>is a Principal Engineering Manager and Applied Scientist currently working in&nbsp;the Machine Teaching group at Microsoft. He has worked in a number of teams in Microsoft&nbsp;including Bing Document Understanding, Local Search and in various innovation teams.</div>
<div><p>Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto.</p><p>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.&nbsp;<i>Agile Machine Learning<b>&nbsp;</b></i>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.</p><p>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.</p><p>What You'll Learn:<br></p><p></p><ul><li>Effectively run a data engineering teamthat is metrics-focused, experiment-focused, and data-focused<br></li><li>Make sound implementation and model exploration decisions based on the data and the metrics<br></li><li>Know the importance of data wallowing: analyzing data in real time in a group setting<br></li><li>Recognize the value of always being able to measure your current state objectively<br></li><li>Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations<br></li></ul><p></p><p>This book is for<b>&nbsp;</b>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.</p></div><div><br></div><div><br></div>
Authors have proven real-world experience with numerous big data projects coordinated across distributed teams for multiple Microsoft markets Teaches you how to manage projects involving machine learning more effectively in a production environment Shows you, by example, how to deliver superior data products through agile processes and organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment

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