AWS Machine Learning Blog
In this post, we share how Domo, a cloud-centered data experiences innovator is using Amazon Bedrock to provide a flexible and powerful AI solution. Getting started with Amazon Bedrock With Amazon Bedrock, teams and individuals can immediately start using FMs without having to worry about provisioning infrastructure or setting up and configuring ML frameworks. Before you get started, verify that your user or role has permission to create or modify Amazon Bedrock resources. For details, see Identity-based policy examples for Amazon Bedrock. To access the models in Amazon Bedrock, on the Amazon Bedrock console, choose Model access in the navigation pane. Review the EULA and enable the FMs you’d like in your account. You can start interacting with the FMs through the following methods: Directly on the Amazon Bedrock console using the Amazon Bedrock playgrounds Programmatically using the Amazon Bedrock API and SDKs In a console terminal using the Amazon Bedrock CLI Conclusion Amazon Bedrock has been instrumental in enhancing data insights and visualization capabilities at Domo through generative AI. By providing flexibility in FM selection, secure access, and a fully managed experience, Amazon Bedrock has enabled Domo to deliver more value to their customers while reducing costs. The service’s security and compliance features have also allowed Domo to serve customers in highly regulated industries. By using Amazon Bedrock, Domo has seen a 50% reduction in cost compared to a similarly performing model from another provider. If you’re ready to start building your own FM innovation with Amazon Bedrock, refer to Getting started with Amazon Bedrock. To learn more about other intriguing Amazon Bedrock applications, see the Amazon Bedrock section of the AWS Machine Learning Blog. About the Authors Joe Clark is software architect for the Domo Labs team and lead architect for Domo’s AI Service Layer, AI Chat, and Model Management. At Domo, Joe has also led development of features including Jupyter Workspaces, Sandbox, and Code Engine. With 15 years of professional software development experience, he has previously worked on IoT and smart city initiatives. Aman Tiwari is a General Solutions Architect working with independent software vendors in the data and generative AI vertical at AWS. He helps them design innovative, resilient, and cost-effective solutions using AWS services. He holds a master’s degree in Telecommunications Networks from Northeastern University. Outside of work, he enjoys playing lawn tennis and reading books. Sindhu Jambunathan is a Senior Solutions Architect at AWS, specializing in supporting ISV customers in the data and generative AI vertical to build scalable, reliable, secure, and cost-effective solutions on AWS. With over 13 years of industry experience, she joined AWS in May 2021 after a successful tenure as a Senior Software Engineer at Microsoft. Sindhu’s diverse background includes engineering roles at Qualcomm and Rockwell Collins, complemented by a Master’s of Science in Computer Engineering from the University of Florida. Her technical expertise is balanced by a passion for culinary exploration, travel, and outdoor activities. Mohammad Tahsin is an AI/ML Specialist Solutions Architect at Amazon Web Services. He lives for staying up to date with the latest technologies in AI/ML and helping guide customers to deploy bespoke solutions on AWS. Outside of work, he loves all things gaming, digital art, and cooking.
Published: 2024-09-09T21:51:23
