August 17, 2020 @ KubeCon + CloudNativeCon Europe Amsterdam 2020
An introduction to Kubeflow, the ML toolkit for K8s and the workflows you can use as a data scientist to scale up your ML code effortlessly.
Ever thought how hard it is to convert your Jupyter Notebooks into deployable and composable pipelines, scale up computation and run hyperparameter tuning? With Kubeflow, this process becomes extremely easy as you make use of the many components of this ML toolkit: Pipelines, Kale, Katib, Snapshot Store.
You will learn how to deploy Kubeflow in minutes, explore your ML code inside a Jupyter Notebook, convert it to a composable and scalable workflow with the click of a button, make the pipeline reproducible using immutable snapshots, go back in history and debug it, run hyperparameter tuning and distribute your computation.
Did we mention you won’t need any specific SDK or CLI command to do this? Sounds like magic? Come and see for yourself!
Ilias Katsakioris is a Software Engineer at Arrikto. He holds a Diploma in Electrical and Computer Engineering from the National Technical University of Athens. He is a Kubernetes and Kubeflow enthusiast, and he has been contributing to the Kubeflow project for almost a year. His main Kubeflow contributions are in the area of Data Management and Data Pipelines. Ilias extended the Kubeflow Pipelines DSL to support K8s Persistent Volumes and Volume Snapshots.
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.