Back to Basics: Build and Deploy Scalable Machine Learning Systems on Open-Source Platforms

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aws
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As organizations mature in their use of AI and machine learning, they tend to build repeatable, efficient, and sustainable processes for model training and deployment. In this episode, Kanwaljit explores Kubeflow basics, why it is used as a ML toolkit on Kubernetes, why customers love it and will demonstrate Kubeflow on AWS (an AWS-specific distribution of Kubeflow) and the value it adds to our customers through integration of highly optimized, cloud-native, enterprise-ready AWS services with Kubeflow.

Additional Resources:
Kubeflow on AWS: https://awslabs.github.io/kubeflow-manifests/
Kubeflow on AWS Deployment options: https://awslabs.github.io/kubeflow-manifests/docs/deployment/
AWS Labs Repository: https://github.com/awslabs/kubeflow-manifests
Kubeflow #AWS Slack Channel: https://www.kubeflow.org/docs/about/community/#slack-community-and-channels

Check out more resources for architecting in the #AWS cloud:
http://amzn.to/3qXIsWN

#AWS #AmazonWebServices #CloudComputing #BackToBasics #MachineLearning
Category
Amazon Web Services
Tags
AWS, Amazon Web Services, Cloud
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