Kubernetized Machine Learning and AI Using KubeFlow

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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