Agile Machine Learning : Effective Machine Learning Inspired by the Agile Manifesto / / by Eric Carter, Matthew Hurst
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| Autore: |
Carter Eric
|
| Titolo: |
Agile Machine Learning : Effective Machine Learning Inspired by the Agile Manifesto / / by Eric Carter, Matthew Hurst
|
| 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 |
| Opac: | Controlla la disponibilità qui |