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Machine learning in production: developing and optimizing data science workflows and applications

The typical data science task in industry starts with an “ask” from the business. But few data scientists have been taught what to do with that ask. This book shows them how to assess it in the context of the business’s goals, reframe it to work optimally for both the data scientist and the employer...

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Detalles Bibliográficos
Autores principales: Kelleher, Andrew, Kelleher, Adam
Lenguaje:eng
Publicado: Addison-Wesley 2019
Materias:
Acceso en línea:http://cds.cern.ch/record/2669253
Descripción
Sumario:The typical data science task in industry starts with an “ask” from the business. But few data scientists have been taught what to do with that ask. This book shows them how to assess it in the context of the business’s goals, reframe it to work optimally for both the data scientist and the employer, and then execute on it. Written by two of the experts who’ve achieved breakthrough optimizations at BuzzFeed, it’s packed with real-world examples that take you from start to finish: from ask to actionable insight.