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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...
| Autores principales: | , |
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| Lenguaje: | eng |
| Publicado: |
Addison-Wesley
2019
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2669253 |
| _version_ | 1780962202169114624 |
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| author | Kelleher, Andrew Kelleher, Adam |
| author_facet | Kelleher, Andrew Kelleher, Adam |
| author_sort | Kelleher, Andrew |
| collection | CERN |
| description | 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. |
| id | cern-2669253 |
| institution | Organización Europea para la Investigación Nuclear |
| language | eng |
| publishDate | 2019 |
| publisher | Addison-Wesley |
| record_format | invenio |
| spelling | cern-26692532021-04-21T18:26:52Zhttp://cds.cern.ch/record/2669253engKelleher, AndrewKelleher, AdamMachine learning in production: developing and optimizing data science workflows and applicationsComputing and ComputersThe 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.Addison-Wesleyoai:cds.cern.ch:26692532019 |
| spellingShingle | Computing and Computers Kelleher, Andrew Kelleher, Adam Machine learning in production: developing and optimizing data science workflows and applications |
| title | Machine learning in production: developing and optimizing data science workflows and applications |
| title_full | Machine learning in production: developing and optimizing data science workflows and applications |
| title_fullStr | Machine learning in production: developing and optimizing data science workflows and applications |
| title_full_unstemmed | Machine learning in production: developing and optimizing data science workflows and applications |
| title_short | Machine learning in production: developing and optimizing data science workflows and applications |
| title_sort | machine learning in production: developing and optimizing data science workflows and applications |
| topic | Computing and Computers |
| url | http://cds.cern.ch/record/2669253 |
| work_keys_str_mv | AT kelleherandrew machinelearninginproductiondevelopingandoptimizingdatascienceworkflowsandapplications AT kelleheradam machinelearninginproductiondevelopingandoptimizingdatascienceworkflowsandapplications |