A Year of Democratizing ML With Kubernetes & Kubeflow - David Aronchick & Fei Xue, Google

A Year of Democratizing ML With Kubernetes & Kubeflow - David Aronchick & Fei Xue, Google

A year ago, we introduced the Kubeflow project to make end-to-end ML pipelines on Kubernetes composable, portable & scalable. Today, thanks to passionate contributors from all over the world, we have the most popular ML platform for Kubernetes. At this Kubecon, we are announcing Kubeflow 1.0, graduating the project to generally available. In this talk, we will cover never before seen features: a web-based UI, simplified setup & sophisticated ML tooling including hyperparameter search and Google's TensorFlow Extended project. Additionally, we will be demonstrating the newly integrated Pipelines project wiring together multi-cloud ML with continuous training and hosted services. Thanks to Kubernetes native extensibility, we are able to bring ML to an entirely new audience, where as long as you can code, you can build complete end-to-end solutions.

A Year of Democratizing ML With Kubernetes & Kubeflow - David Aronchick & Fei Xue, Google

A year ago, we introduced the Kubeflow project to make end-to-end ML pipelines on Kubernetes composable, portable & scalable. Today, thanks to passionate contributors from all over the world, we have the most popular ML platform for Kubernetes. At this Kubecon, we are announcing Kubeflow 1.0, graduating the project to generally available. In this talk, we will cover never before seen features: a web-based UI, simplified setup & sophisticated ML tooling including hyperparameter search and Google's TensorFlow Extended project. Additionally, we will be demonstrating the newly integrated Pipelines project wiring together multi-cloud ML with continuous training and hosted services. Thanks to Kubernetes native extensibility, we are able to bring ML to an entirely new audience, where as long as you can code, you can build complete end-to-end solutions.

kubernetes kubeflow machine-learning

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