Rachel Silver
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Kubernetized Machine Learning and AI Using KubeFlow

8/28/2018

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In my previous blog, End-to-End Machine Learning Using Containerization, I covered the advantages of doing machine learning using microservices and how containerization can improve every step of the workflow by providing:
  • Personalized Development Environments
  • Agile Training Capabilities
  • Microservices Frameworks for Model Serving
Today, I'd like to talk about an example framework, which we're hearing a lot of buzz about, from the community called KubeFlow. The KubeFlow infrastructure provides the means to deploy best-of-breed open source systems for machine learning to any cluster running Kubernetes, whether on-premises or in the cloud.
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    Rachel Silver is the Product Management Lead for Machine Learning & AI @ MapR Data Technologies. 
    This blog is unaffiliated with her employer and does not represent their views.

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