AWS Machine Learning Blog Today, we’re pleased to announce the general availability (GA) of Amazon Bedrock Custom Model Import. This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API. Whether leveraging fine-tuned models like Meta Llama, Mistral Mixtral, and IBM Granite, or developing proprietary […]Continue reading

AWS Machine Learning Blog Generative AI adoption among various industries is revolutionizing different types of applications, including image editing. Image editing is used in various sectors, such as graphic designing, marketing, and social media. Users rely on specialized tools for editing images. Building a custom solution for this task can be complex. However, by using […]Continue reading

AWS Machine Learning Blog Many organizations are building generative AI applications powered by large language models (LLMs) to boost productivity and build differentiated experiences. These LLMs are large and complex and deploying them requires powerful computing resources and results in high inference costs. For businesses and researchers with limited resources, the high inference costs of […]Continue reading

AWS Machine Learning Blog In Part 1 of this series, we explored best practices for creating accurate and reliable agents using Amazon Bedrock Agents. Amazon Bedrock Agents help you accelerate generative AI application development by orchestrating multistep tasks. Agents use the reasoning capability of foundation models (FMs) to create a plan that decomposes the problem […]Continue reading

AWS Machine Learning Blog This post is co-written by Rodrigo Amaral, Ashwin Murthy and Meghan Stronach from Qualcomm. In this post, we introduce an innovative solution for end-to-end model customization and deployment at the edge using Amazon SageMaker and Qualcomm AI Hub. This seamless cloud-to-edge AI development experience will enable developers to create optimized, highly […]Continue reading