Getting started with Kubeflow
Who is this presentation for?Data Scientists and AI Infrastructure Engineers
Prerequisite knowledgeKnowledge of Kubernetes and various machine learning frameworks like Tensorflow and Pytorch would be extremely useful but is not required. The ability to clone a GitHub repo is crucial
Materials or downloads needed in advance
What you'll learn
Kubeflow consists of dozens of components used to train and serve machine learning models. Our first task will be to understand the Kubeflow architecture and how it fits into the larger Kubernetes ecosystem. We will discuss in detail the performance, availability, and security impacts of various Kubeflow deployment options and decisions that you might make in how you use Kubeflow.
Next, we will go hands on and actually deploy a subset of the Kubeflow components into a running Kubernetes environment. We will then use Jupyter Hub and Jupyter notebooks to import our training data and set up prerequisites for creating training environments using Tensorflow (and PyTorch time permitting) Finally, we will being to submit training jobs and generate models. We will serve these models via Tensorflow Serving (and Seldon Core time permitting)
Skyler Thomas is an engineer at MapR, where he is designing Kubernetes-based infrastructure to deliver machine learning and big data applications at scale. Previously, Skyler was an architect at IBM, where he worked with more than a hundred customers to deliver extreme-scale applications in the healthcare, financial services, and retail industries.
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