Turning ML into magical products often requires complex, distributed systems that bring with them a unique, ML specific set of infrastructure problems. A year ago, we started building Kubeflow to leverage Kubernetes to solve these problems. In this talk, we will use the example of a search engine for code using natural language (http://bit.ly/gh-kf-search) to illustrate how Kubeflow and Kubernetes can be used to build and deploy ML products.
Jeremy Lewi is a co-founder and lead engineer at Google for the Kubeflow project, an effort to help developers and enterprises deploy and use ML cloud-natively everywhere. He’s been building on Kubernetes since its inception starting with Dataflow and then moving onto Cloud ML Engine and now Kubeflow.
Hamel Husain is a Senior Data Scientist at Github who is focused on creating the next generation of developer tools powered by machine learning. His work involves extensive use of natural language and deep learning techniques to extract features from code and text. Prior to Github, Hamel was a Data Scientist at Airbnb where he worked on growth marketing and at DataRobot where he helped build automated machine learning tools for data scientists.
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