Putting Machine Learning (ML) into production is not trivial and the actual ML capability is just a tiny part of the entire AI/ML lifecycle. This requires new platforms for collaboration between data scientists and operations engineers (e.g. SREs).

In this webinar, we will introduce Kubeflow, a platform for managing AI/ML lifecycles on Kubernetes. Kubeflow is a set of core applications needed to efficiently develop, build, train, and deploy models on Kubernetes. These capabilities make it easy to train and tune models, deploy ML workloads anywhere, etc.

This webinar will start with identifying key customer/user pain points. Next, the core features of the Kubeflow 1.0 release – and how they address those issues – will be covered. Finally, we will look into our crystal ball to share possible feature enhancements as well as opportunities for increased community participation.