Until recently, data scientists had to spend significant time performing operational tasks, such as ensuring frameworks, runtimes, and drivers for CPUs and GPUs are working well together. They are also needed to design and build machine learning (ML) pipelines to orchestrate complex workflows for deploying ML models in production. Kubeflow is dedicated to making ML deployments on Kubernetes simple, portable, and scalable. In this session, learn how you can leverage Kubeflow on Amazon EKS to deploy best-of-breed open source machine learning systems to provide data scientists with all the tools they need to run machine learning in the cloud. To conclude, we leverage Kubeflow on Amazon EKS and dive into notebooks, model training, AutoML, workflows, and model serving.
Speaker: Ben Friebe, ISV Solutions Architect, AWS