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An introduction to machine learning models in production : how to transition from one-off models to reproducible pipelines /
"This course lays out the common architecture, infrastructure, and theoretical considerations for managing an enterprise machine learning (ML) model pipeline. Because automation is the key to effective operations, you'll learn about open source tools like Spark, Hive, ModelDB, and Docker a...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly,
2017.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Slepicka, Jason, |e on-screen presenter. | |
| 245 | 1 | 3 | |a An introduction to machine learning models in production : |b how to transition from one-off models to reproducible pipelines / |c with Jason Slepicka. |
| 264 | 1 | |a [Place of publication not identified] : |b O'Reilly, |c 2017. | |
| 300 | |a 1 online resource (1 streaming video file (39 min., 56 sec.)) | ||
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| 500 | |a Title from title screen (Safari, viewed January 15, 2018). | ||
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| 511 | 0 | |a Presenter, Jason Slepicka. | |
| 520 | |a "This course lays out the common architecture, infrastructure, and theoretical considerations for managing an enterprise machine learning (ML) model pipeline. Because automation is the key to effective operations, you'll learn about open source tools like Spark, Hive, ModelDB, and Docker and how they're used to bridge the gap between individual models and a reproducible pipeline. You'll also learn how effective data teams operate; why they use a common process for building, training, deploying, and maintaining ML models; and how they're able to seamlessly push models into production. The course is designed for the data engineer transitioning to the cloud and for the data scientist ready to use model deployment pipelines that are reproducible and automated. Learners should have basic familiarity with: cloud platforms like Amazon Web Services; Scala or Python; Hadoop, Spark, or Pandas; SBT or Maven; Bash, Docker, and REST."--Resource description page | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Cloud computing. | |
| 650 | 0 | |a Quantitative research. | |
| 650 | 0 | |a Mathematical statistics |x Data processing. | |
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| 650 | 6 | |a Statistique mathématique |x Informatique. | |
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| 650 | 7 | |a Machine learning |2 fast | |
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