Agile Machine Learning : Effective Machine Learning Inspired by the Agile Manifesto / / by Eric Carter, Matthew Hurst

• Autore: Carter Eric
• Pubblicazione: Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019
• Edizione: 1st ed. 2019.
• Descrizione fisica: 1 online resource (257 pages) : illustrations
• Disciplina: 004.165
• Soggetto topico: Microsoft software; Microsoft .NET Framework; Software engineering; Big data; Microsoft and .NET; Software Engineering; Big Data
• Persona (resp. second.): HurstMatthew
• Note generali: Includes index.
• Nota di contenuto: 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.
• Sommario/riassunto: 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 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.
• Titolo autorizzato: Agile Machine Learning
• ISBN: 1-4842-5107-5
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910338230703321
• Lo trovi qui: Univ. Federico II