Build ML workflows on Kubernetes and Amazon SageMaker with Kubeflow Pipelines

This session takes you through the journey of building Enterprise Scale ML workflows on Kubernetes and Amazon SageMaker with Kubeflow Pipelines.

Kubeflow is a popular open-source machine learning (ML) toolkit for Kubernetes users who want to build custom ML pipelines. Kubeflow Pipelines is an add-on to Kubeflow that lets you build and deploy portable and scalable end-to-end ML workflows. In this session, learn how to get started with Kubeflow Pipelines on AWS. See how you can integrate powerful Amazon SageMaker features such as data labeling, large-scale hyperparameter tuning, distributed training jobs, and secure and scalable model deployment using SageMaker Components for Kubeflow Pipelines.

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. In this session, we dive into Amazon SageMaker and container technologies and discuss how easy it is to integrate tasks such as model training and deployment into Kubernetes and Kubeflow-based ML pipelines. Kubeflow Pipelines is an add-on to Kubeflow that allows you to build and deploy portable and scalable end-to-end ML workflows. In this session, learn how you can integrate Amazon SageMaker features such as data labeling, large-scale hyperparameter tuning, distributed training jobs, and secure scalable model deployment using SageMaker Components for Kubeflow Pipelines.

#kubernetes #k8s #kubeflow #sagemaker

Written by admin