Loading…
Tuesday, May 21 • 14:00 - 15:25
Tutorial: Introduction to Kubeflow Pipelines - Michelle Casbon, Dan Sanche, Dan Anghel, & Michal Zylinski, Google (Limited Availability; First-Come, First-Served Basis)

Sign up or log in to save this to your schedule and see who's attending!

Feedback form is now closed.
In this session, you will learn how to install and use Kubeflow Pipelines to create a full machine learning application on Kubernetes.

Starting with an empty environment, you will create a Kubernetes cluster and install Kubeflow from scratch. Then you will build and run a full pipeline that first trains a model using TensorFlow, then serves the model, and finally deploys a web front-end for interacting with the resulting predictions. You will then move into a notebook to build and run your pipeline using the Python SDK.

You will become familiar with Google Cloud Platform tools such as Cloud Shell and Kubernetes Engine.

Prerequisite: fundamental knowledge of Kubernetes.
Setup: must bring own laptop. Qwiklab/GCP credits will be provided.

Note: this session showcases Kubeflow features newly released since the Seattle workshop.

Speakers
avatar for Michelle Casbon

Michelle Casbon

Senior Engineer, Google
Michelle Casbon is a Senior Engineer at Google, where she focuses on open source for machine learning and big data tools. Prior to joining Google, she was at Qordoba as Director of Data Science and Idibon as a Senior Data Science Engineer. Within these roles, she built and shipped... Read More →
avatar for Dan Anghel

Dan Anghel

Strategic Cloud Engineer, Google
Dan joined Google Paris 3 years ago after a more than 10 years long adventure in Retail. Specialized in Big Data and Machine Learning, he is helping the largest Google customers accelerate their journey into the Cloud.
MZ

Michal Zylinski

Cloud Customer Engineer, Google
avatar for Dan Sanche

Dan Sanche

Developer Programs Engineer, Google
Dan is a DevRel Enginner at Google focused on improving the developer experience of GCP DevOps products, with a particular interest in Machine learning infrastructure



Tuesday May 21, 2019 14:00 - 15:25
Hall 8.0 F5