Kubeflow, the Kubernetes native application for AI and Machine Learning, continues to accelerate feature additions and community growth. The community has released two new versions since the last Kubecon – 0.4 in January and 0.5 in April – and is currently working on the 0.6 release, to be released in July. The key features in each release are briefly discussed below.
For those attending KubeCon + CloudNativeCon Europe 2019 in Barcelona, you can learn more about Kubeflow and how to apply it to your business in the following sessions:
|14:00||Kubernetes the New Research Platform|
– Lindsey Tulloch, Brock University & Bob Killen, University of Michigan
|14:00||Tutorial: Introduction to Kubeflow Pipelines|
– Michelle Casbon, Dan Sanche, Dan Anghel, & Michal Zylinski, Google
|15:55||KubeFlow BoF (Birds of a Feather)|
– David Aronchick, Microsoft & Yaron Haviv, Iguazio
|11:55||Towards Kubeflow 1.0, Bringing a Cloud Native Platform For ML to Kubernetes |
– David Aronchick, Microsoft & Jeremy Lewi, Google
|14:00||Building Cross-Cloud ML Pipelines with Kubeflow with Spark & Tensorflow|
– Holden Karau, Google & Trevor Grant, IBM
|14:50||Managing Machine Learning in Production with Kubeflow and DevOps|
– David Aronchick, Microsoft
|11:55||A Tale of Two Worlds: Canary-Testing for Both ML Models and Microservices |
– Jörg Schad, ArangoDB & Vincent Lesierse, Vamp.io
|14:00||Moving People and Products with Machine Learning on Kubeflow |
– Jeremy Lewi, Google & Willem Pienaar, GO-JEK
|14:50||Economics and Best Practices of Running AI/ML Workloads on Kubernetes |
– Maulin Patel, Google & Yaron Haviv, Iguazio
You can learn more about the 0.5 release from the Kubeflow blog on 0.5.
You can learn more about the 0.4 release from the Kubeflow blog on 0.4.
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