Agile Machine Learning
Apress (Verlag)
978-1-4842-5106-5 (ISBN)
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.
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.
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.
| Erscheinungsdatum | 06.09.2019 |
|---|---|
| Zusatzinfo | 35 Illustrations, black and white; XVII, 248 p. 35 illus. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Software Entwicklung ► Agile Software Entwicklung | |
| Schlagworte | agile project management • Agile software development • AI • Artificial Intelliegence • Big Data • Data and Analytics • Data Science • inferences • judge manangement • machine learning • Machine Learning Best Practices • Managing mixed developer teams • Metrics and Measurement • Statistics |
| ISBN-10 | 1-4842-5106-7 / 1484251067 |
| ISBN-13 | 978-1-4842-5106-5 / 9781484251065 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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