Cargando…
Machine learning at enterprise scale : how real practitioners handle six common challenges /
Enterprises in traditional and emerging industries alike are increasingly turning to machine learning (ML) to maximize the value of their business data. But many of these teams are likely to experience significant hurdles and setbacks throughout the journey. In this practical ebook, data scientists...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media,
[2019]
|
| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | OR_on1112253806 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr unu|||||||| | ||
| 008 | 190815s2019 caua o 000 0 eng d | ||
| 040 | |a UMI |b eng |e rda |e pn |c UMI |d OCLCF |d CZL |d OCLCQ |d OCLCO |d OCLCQ |d OCLCO | ||
| 020 | |z 9781492050803 | ||
| 035 | |a (OCoLC)1112253806 | ||
| 037 | |a CL0501000065 |b Safari Books Online | ||
| 050 | 4 | |a Q325.5 | |
| 049 | |a UAMI | ||
| 100 | 1 | |a Cinquegrana, Piero, |e author. | |
| 245 | 1 | 0 | |a Machine learning at enterprise scale : |b how real practitioners handle six common challenges / |c Piero Cinquegrana and Matheen Raza. |
| 250 | |a First edition. | ||
| 264 | 1 | |a Sebastopol, CA : |b O'Reilly Media, |c [2019] | |
| 264 | 4 | |c Ã2019 | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from title page (Safari, viewed August 13, 2019). | |
| 520 | |a Enterprises in traditional and emerging industries alike are increasingly turning to machine learning (ML) to maximize the value of their business data. But many of these teams are likely to experience significant hurdles and setbacks throughout the journey. In this practical ebook, data scientists and machine learning engineers explore six common challenges that teams face every day when creating, managing, and scaling ML applications. For each problem, you'll get hard-earned advice from Hussein Mehanna, AI engineering director for Google Cloud; Nakul Arora, VP of product management and marketing at Infosys; Patrick Hall, senior director for data science products at H2O; Matt Harrison, consultant and corporate trainer at MetaSnake; Joao Natali, data science director at Neustar; and Jerry Overton, data scientist and technology fellow at DXC. Accomplished data scientist Piero Cinquegrana and Matheen Raza of Qubole examine ways to overcome challenges that include: Reconciling disparate interfaces Resolving environment dependencies Ensuring close collaboration among all ML stakeholders Building or renting adequate ML infrastructure Meeting the scalability needs of your application Enabling smooth deployment of ML projects. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Information technology |x Management. | |
| 650 | 0 | |a Business enterprises |x Technological innovations. | |
| 650 | 2 | |a Artificial Intelligence | |
| 650 | 2 | |a Machine Learning | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 6 | |a Technologie de l'information |x Gestion. | |
| 650 | 6 | |a Entreprises |x Innovations. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 650 | 7 | |a Business enterprises |x Technological innovations |2 fast | |
| 650 | 7 | |a Information technology |x Management |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 700 | 1 | |a Raza, Matheen, |e author. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781492050810/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||