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Kubeflow Explained: NLP Architectures on Kubernetes

YOW! 2018 Brisbane

There's more to a Natural Language Processing (NLP) application than an ensemble of models. Much more! Like any traditional application, there is an entire ecosystem of supporting tools that enables core ML functionality. How do you choose the best ones? Which ones can you do without? How do you maintain context when the complexity of such a system gets out of hand?

In this session, you will learn how to deploy the full scope of an NLP application on Kubernetes with Kubeflow. The guiding principles of a robust and resilient system are explained and used as the foundation for defining a specific architecture. Techniques for modifying and maintaining it over time are described. Find out what Kubeflow currently supports and the long-term vision for the project, presented by a project contributor.

Michelle Casbon

Sr. Engineer

Google

United States

Michelle Casbon is a Senior Engineer on the Google Cloud Platform Developer Relations team, where she focuses on open source contributions and community engagement for machine learning and big data tools. Prior to joining Google, she was at several San Francisco-based startups as a Senior Engineer and Director of Data Science. Within these roles, she built and shipped machine learning products on distributed platforms using both AWS and GCP. Michelle’s development experience spans more than a decade and has primarily focused on multilingual natural language processing, system architecture and integration, and continuous delivery pipelines for machine learning applications. She especially loves working with open source projects and is an active contributor to Kubeflow. Michelle holds a masters degree from the University of Cambridge.