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Record Nr. |
UNINA9910338230703321 |
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Autore |
Carter Eric |
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Titolo |
Agile Machine Learning : Effective Machine Learning Inspired by the Agile Manifesto / / by Eric Carter, Matthew Hurst |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (257 pages) : illustrations |
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Disciplina |
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Soggetti |
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Microsoft software |
Microsoft .NET Framework |
Software engineering |
Big data |
Microsoft and .NET |
Software Engineering |
Big Data |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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. |
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