9 September 12:00 - 12:45
Building Machine Learning with Kubeflow and serving them with Kfserving.
Head of Engineering | Go Developer | Lecturer & Speaker
A big part of Machine Learning projects is about engineering. We need to prepare data, build models, retrain them, not to mention about scaling the whole process and keeping it deterministic. Kubeflow helps us with it bringing best practices to manage and deploy such workflows. The second part of the engineering starts when the models are ready, and we need to bring them to production and operate them. In this talk, we will look into Kfserving, new sibling project to kubeflow, that brings benefits of Knative and Istio to deploy, scale, and monitor our Machine Learning components.
The complete Kubeflow might be overkill for smaller teams in the beginning, thus we will also show how to start smaller and pick just necessary components from kubeflow community, such as Argo and Tensorserve.