Agile Machine Learning
Carter, Eric, Hurst, Matthew
Produktnummer:
1873f6f95d05dc4b67b3046a76ebf23001
Autor: | Carter, Eric Hurst, Matthew |
---|---|
Themengebiete: | AI Agile Software Development Big Data Data and Analytics Inferences Machine Learning Machine Learning Best Practices Metrics and Measurement Statistics judge manangement |
Veröffentlichungsdatum: | 22.08.2019 |
EAN: | 9781484251065 |
Sprache: | Englisch |
Seitenzahl: | 248 |
Produktart: | Kartoniert / Broschiert |
Verlag: | APRESS |
Untertitel: | Effective Machine Learning Inspired by the Agile Manifesto |
Produktinformationen "Agile Machine Learning"
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 LearnEffectively run a data engineeringteam that is metrics-focused, experiment-focused, and data-focusedMake sound implementation and model exploration decisions based on the data and the metricsKnow the importance of data wallowing: analyzing data in real time in a group settingRecognize the value of always being able to measure your current state objectivelyUnderstand data literacy, a key attribute of a reliable data engineer, from definitions to expectationsWho This Book Is ForAnyone 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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