AWS Machine Learning Blog
In this post, we discuss Bria’s family of models, explain the Amazon SageMaker platform, and walk through how to discover, deploy, and run inference on a Bria 2.3 model using SageMaker JumpStart. Clean up After you’re done running the notebook, delete all resources that you created in the process so your billing is stopped. Use the following code: predictor.delete_model() predictor.delete_endpoint() Conclusion With the availability of Bria 2.3, 2.2 HD, and 2.3 Fast in SageMaker JumpStart and AWS Marketplace, enterprises can now use advanced generative AI capabilities to enhance their visual content creation processes. These models provide a balance of quality, speed, and compliance, making them an invaluable asset for any organization looking to stay ahead in the competitive landscape. Bria’s commitment to responsible AI and the robust security framework of SageMaker provide enterprises with the full package for data privacy, regulatory compliance, and responsible AI models for commercial use. In addition, the integrated experience takes advantage of the capabilities of both platforms to simplify MLOps, data storage, and real-time processing. For more information about using FMs in SageMaker JumpStart, refer to Train, deploy, and evaluate pretrained models with SageMaker JumpStart, JumpStart Foundation Models, and Getting started with Amazon SageMaker JumpStart. Explore Bria models in SageMaker JumpStart today and revolutionize your visual content creation process! About the Authors Bar Fingerman is the Head of AI/ML Engineering at Bria. He leads the development and optimization of core infrastructure, enabling the company to scale cutting-edge generative AI technologies. With a focus on designing high-performance supercomputers for large-scale AI training, Bar leads the engineering group in deploying, managing, and securing scalable AI/ML cloud solutions. He works closely with leadership and cross-functional teams to align business goals while driving innovation and cost-efficiency. Supriya Puragundla is a Senior Solutions Architect at AWS. She has over 15 years of IT experience in software development, design, and architecture. She helps key customer accounts on their data, generative AI, and AI/ML journeys. She is passionate about data-driven AI and the area of depth in ML and generative AI. Rodrigo Merino is a Generative AI Solutions Architect Manager at AWS. With over a decade of experience deploying emerging technologies, ranging from generative AI to IoT, Rodrigo guides customers across various industries to accelerate their AI/ML and generative AI journeys. He specializes in helping organizations train and build models on AWS, as well as operationalize end-to-end ML solutions. Rodrigo’s expertise lies in bridging the gap between cutting-edge technology and practical business applications, enabling companies to harness the full potential of AI and drive innovation in their respective fields. Eliad Maimon is a Senior Startup Solutions Architect at AWS, focusing on generative AI startups. He helps startups accelerate and scale their AI/ML journeys by guiding them through deep-learning model training and deployment on AWS. With a passion for AI and entrepreneurship, Eliad is committed to driving innovation and growth in the startup ecosystem.
Published: 2024-10-15T20:05:38
