November 20, 2020 @ KubeCon + CloudNativeCon North America Boston 2020
A hands-on lab-based tutorial to show Data Scientists and ML Engineers alike how to turbocharge your Kubeflow efforts.
In this session, you will learn how to quickly build, tune, and execute complex Kubeflow workflows – as well as how to work faster using Kale to automate much of your work.
Learn how to rapidly automate Kubeflow:
- Deploy a Jupyter Notebook as a Kubeflow pipeline using Kale – Optimize your model training using Katib for hyperparameter tuning
- Serve your model with KFServing – Run thousands of runs with caching and garbage collection
- Track and reproduce pipeline steps along with their state and artifacts Data Scientists benefit from an intuitive GUI that automates and hides all of the underlying infrastructure and SDK requirements.
ML Engineers can use the reproducible, automated workflows as a scaffold to quickly move to even more advanced tuning and model building.
This talk is a lab tutorial you can follow
Cloud AI Advocacy Manager @ Google
Karl Weinmeister is a Cloud AI Advocacy Manager at Google, where he leads a team of data science experts who develop content and engage with communities worldwide. Karl has worked extensively in machine learning and cloud technologies. He was a contributor to one of the first AI-based crossword puzzle solvers that are still referenced today.
Stefano Fioravanzo is a Software Engineer at Arrikto, his background is in Data Science and ML Research. He understands the value of building robust Machine Learning infrastructure and providing Data Scientist with the necessary tools to scale up their workflows. He works as a full-time contributor to Kubeflow and he is the creator of the Kubeflow Kale project which enables Jupyter Notebooks deployments to Kubeflow Pipelines.