Deploying machine learning models as microservices using Docker : a REST-based architecture for serving ML model outputs at scale /
"Modern applications running in the cloud often rely on REST-based microservices architectures by using Docker containers. Docker enables your applications to communicate between one another and to compose and scale various components. Data scientists use these techniques to efficiently scale t...
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| Main Author: | |
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| Format: | Electronic Video |
| Language: | English |
| Published: |
[Place of publication not identified] :
O'Reilly,
2017.
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| Subjects: | |
| Online Access: | CONNECT |
MARC
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| 245 | 1 | 0 | |a Deploying machine learning models as microservices using Docker : |b a REST-based architecture for serving ML model outputs at scale / |c with Hollin Wilkins & Jason Slepicka. |
| 246 | 3 | 0 | |a REST-based architecture for serving ML model outputs at scale |
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| 520 | |a "Modern applications running in the cloud often rely on REST-based microservices architectures by using Docker containers. Docker enables your applications to communicate between one another and to compose and scale various components. Data scientists use these techniques to efficiently scale their machine learning models to production applications. This video teaches you how to deploy machine learning models behind a REST API, to serve low latency requests from applications, without using a Spark cluster. In the process, you'll learn how to export models trained in SparkML; how to work with Docker, a convenient way to build, deploy, and ship application code for microservices; and how a model scoring service should support single on-demand predictions and bulk predictions. Learners should have basic familiarity with the following: Scala or Python; Hadoop, Spark, or Pandas; SBT or Maven; cloud platforms like Amazon Web Services; Bash, Docker, and REST."--Resource description page | ||
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