Agile Machine Learning: Effective Machine Learning Inspired by the Agile Manifesto (1st ed.)
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- Synopsis
- 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-focusedMake sound implementation and model exploration decisions based on the data and the metricsKnow the importance of data wallowing: analyzing data in real time in a group settingRecognize the value of always being able to measure your current state objectivelyUnderstand data literacy, a key attribute of a reliable data engineer, from definitions to expectations Who This Book Is ForAnyone 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.
- Copyright:
- 2019
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9781484251072
- Related ISBNs:
- 9781484251065
- Publisher:
- Apress
- Date of Addition:
- 09/01/19
- Copyrighted By:
- Springer
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Computers and Internet
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.