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Beschreibung
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 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. 80,24 €
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Technische Daten
Erscheinungsdatum
22.08.2019
Sprache
Englisch
EAN
9781484251065
Herausgeber
APRESS
Sonderedition
Nein
Autor
Eric Carter
Seitenanzahl
248
Auflage
1st edition
Einbandart
Taschenbuch
Buch Untertitel
Effective Machine Learning Inspired by the Agile Manifesto
Schlagwörter
Agile Software Development, Machine Learning, Data and Analytics, Machine Learning Best Practices, Big Data, Statistics, Inferences, Metrics and Measurement, judge manangement, AI, Artificial Intelliegence, Managing mixed developer teams, Data Science, Agile Project Management
Thema-Inhalt
UMP - Microsoftprogrammierung
UMZ - Software Engineering
UN - Datenbanken
Höhe
254 mm
Breite
17.8 cm
Hersteller: Apress, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com, Springer Nature Customer Service Center GmbH
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