Today, we are announcing the preview of Amazon Q, a new type of generative artificial intelligence (AI) powered assistant that is specifically for work and can be tailored to a customer’s business. Amazon Q brings a set of capabilities to support developers and IT professionals. Now you can use Amazon Q to get started building applications on AWS, research best practices, resolve errors, and get assistance in coding new features for your applications. For example, Amazon Q Code Transformation can perform Java application upgrades now, from version 8 and 11 to version 17. Amazon Q is available in multiple areas of AWS to provide quick access to answers and ideas wherever you work. Here’s a quick look at Amazon Q,…
Month: November 2023
Guardrails for Amazon Bedrock helps implement safeguards customized to your use cases and responsible AI policies (preview)
As part of your responsible artificial intelligence (AI) strategy, you can now use Guardrails for Amazon Bedrock (preview) to promote safe interactions between users and your generative AI applications by implementing safeguards customized to your use cases and responsible AI policies. AWS is committed to developing generative AI in a responsible, people-centric way by focusing on education and science and helping developers to integrate responsible AI across the AI lifecycle. With Guardrails for Amazon Bedrock, you can consistently implement safeguards to deliver relevant and safe user experiences aligned with your company policies and principles. Guardrails help you define denied topics and content filters to remove undesirable and harmful content from interactions between users and your applications. This provides an additional…
Agents for Amazon Bedrock is now available with improved control of orchestration and visibility into reasoning
Back in July, we introduced Agents for Amazon Bedrock in preview. Today, Agents for Amazon Bedrock is generally available. Agents for Amazon Bedrock helps you accelerate generative artificial intelligence (AI) application development by orchestrating multistep tasks. Agents uses the reasoning capability of foundation models (FMs) to break down user-requested tasks into multiple steps. They use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledge bases using Retrieval Augmented Generation (RAG) to provide a final response to the end user. If you’re curious how this works, check out my previous posts on agents that include a primer on advanced reasoning and a primer on RAG. Starting today, Agents for…
Customize models in Amazon Bedrock with your own data using fine-tuning and continued pre-training
Today, I’m excited to share that you can now privately and securely customize foundation models (FMs) with your own data in Amazon Bedrock to build applications that are specific to your domain, organization, and use case. With custom models, you can create unique user experiences that reflect your company’s style, voice, and services. With fine-tuning, you can increase model accuracy by providing your own task-specific labeled training dataset and further specialize your FMs. With continued pre-training, you can train models using your own unlabeled data in a secure and managed environment with customer managed keys. Continued pre-training helps models become more domain-specific by accumulating more robust knowledge and adaptability—beyond their original training. Let me give you a quick tour of…