Agile Machine Learning

• Verfasst von: Carter, Eric [VerfasserIn]
• Titel: Agile Machine Learning
• Titelzusatz: Effective Machine Learning Inspired by the Agile Manifesto
• Mitwirkende: Hurst, Matthew [MitwirkendeR]
• Institutionen: Safari, an O’Reilly Media Company
• Verf.angabe: Carter, Eric.
• Ausgabe: 1st edition
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: Apress
• Jahr: 2019
• Umfang: 1 online resource (257 pages)
• Fussnoten: Online resource; Title from title page (viewed August 21, 2019)
• ISBN: 978-1-4842-5107-2
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Electronic books ; local
• Sach-SW: Electronic books
• K10plus-PPN: 1684789788
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m3606712677
• Lokale URL Inst.: Zum Volltext

Abstract

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.