Tag: Danilo Poccia

This was a busy week for Amazon Bedrock with many new features! Using GitHub Actions with AWS CodeBuild is much easier. Also, Amazon Q in Amazon CodeCatalyst can now manage more complex issues. I was amazed to meet so many new and old friends at the AWS Summit London. To give you a quick glimpse, here’s AWS Hero Yan Cui starting his presentation at the AWS Community stage. Last week’s launches With so many interesting new features, I start with generative artificial intelligence (generative AI) and then move to the other topics. Here’s what got my attention: Amazon Bedrock – For supported architectures such as Llama, Mistral, or Flan T5, you can now import custom models and access them on demand.…

With Agents for Amazon Bedrock, applications can use generative artificial intelligence (generative AI) to run tasks across multiple systems and data sources. Starting today, these new capabilities streamline the creation and management of agents: Quick agent creation – You can now quickly create an agent and optionally add instructions and action groups later, providing flexibility and agility for your development process. Agent builder – All agent configurations can be operated in the new agent builder section of the console. Simplified configuration – Action groups can use a simplified schema that just lists functions and parameters without having to provide an API schema. Return of control –You can skip using an AWS Lambda function and return control to the application invoking…

With Amazon Bedrock, you have access to a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies that make it easier to build and scale generative AI applications. Some of these models provide publicly available weights that can be fine-tuned and customized for specific use cases. However, deploying customized FMs in a secure and scalable way is not an easy task. Starting today, Amazon Bedrock adds in preview the capability to import custom weights for supported model architectures (such as Meta Llama 2, Llama 3, and Mistral) and serve the custom model using On-Demand mode. You can import models with weights in Hugging Face safetensors format from Amazon SageMaker and Amazon Simple Storage Service (Amazon S3). In…

Industries like automotive, robotics, and finance are increasingly implementing computational workloads like simulations, machine learning (ML) model training, and big data analytics to improve their products. For example, automakers rely on simulations to test autonomous driving features, robotics companies train ML algorithms to enhance robot perception capabilities, and financial firms run in-depth analyses to better manage risk, process transactions, and detect fraud. Some of these workloads, including simulations, are especially complicated to run due to their diversity of components and intensive computational requirements. A driving simulation, for instance, involves generating 3D virtual environments, vehicle sensor data, vehicle dynamics controlling car behavior, and more. A robotics simulation might test hundreds of autonomous delivery robots interacting with each other and other systems…