Imagine this—all employees relying on generative artificial intelligence (AI) to get their work done faster, every task becoming less mundane and more innovative, and every application providing a more useful, personal, and engaging experience. To realize this future, organizations need more than a single, powerful large language model (LLM) or chat assistant. They need a full range of capabilities to build and scale generative AI applications that are tailored to their business and use case —including apps with built-in generative AI, tools to rapidly experiment and build their own generative AI apps, a cost-effective and performant infrastructure, and security controls and guardrails. That’s why we are investing in a comprehensive generative AI stack. At the top layer, which includes generative AI-powered applications, we have Amazon Q, the most capable generative AI-powered assistant. The middle layer has Amazon Bedrock, which provides tools to easily and rapidly build, deploy, and scale generative AI applications leveraging LLMs and other foundation models (FMs). And at the bottom, there’s our resilient, cost-effective infrastructure layer, which includes chips purpose-built for AI, as well as Amazon SageMaker to build and run FMs. All of these services are secure by design, and we keep adding features that are critical to deploying generative AI applications tailored to your business. During the last 18 months, we’ve launched more than twice as many machine learning (ML) and generative AI features into general availability than the other major cloud providers combined. That’s another reason why hundreds of thousands of customers are now using our AI services.
Today at the AWS New York Summit, we announced a wide range of capabilities for customers to tailor generative AI to their needs and realize the benefits of generative AI faster. We’re enabling anyone to build generative AI applications with Amazon Q Apps by writing a simple natural language prompt—in seconds. We’re making it easier to leverage your data, supercharge agents, and quickly, securely, and responsibly deploy generative AI into production with new features in Amazon Bedrock. And we announced new partnerships with innovators like Scale AI to help you customize your applications quickly and easily.
Generative AI-powered apps transform business as usual
Generative AI democratizes information, gives more people the ability to create and innovate, and provides access to productivity-enhancing assistance that was never available before. That’s why we’re building generative AI-powered applications for everyone.
Amazon Q, which includes Amazon Q Developer and Amazon Q Business, is the most capable generative AI-powered assistant for software development and helping employees make better decisions—faster—leveraging their company’s data. Not only does Amazon Q generate the industry’s most accurate coding suggestions, it can also autonomously perform multistep tasks like upgrading Java applications and generating and implementing new features. Amazon Q is where developers need it on the AWS Management Console and in popular integrated development environments, including IntelliJ IDEA, Visual Studio, VS Code, and Amazon SageMaker Studio. You can securely customize Amazon Q Developer with your internal code base to get more relevant and useful recommendations for in-line coding and save even more time. For instance, National Australia Bank has seen increased acceptance rates of 60%, up from 50% and Amazon Prime developers have already seen a 30% increase in acceptance rates. Amazon Q can also help employees do more with the vast troves of data and information contained in their company’s documents, systems, and applications by answering questions, providing summaries, generating business intelligence (BI) dashboards and reports, and even generating applications that automate key tasks. We’re super excited about the productivity gains customers and partners have seen, with early signals that Amazon Q could help their employees become over 80% more productive at their jobs.
To enable all employees to create their own generative AI applications to automate tasks, today we announced the general availability of Amazon Q Apps, a feature of Amazon Q Business. With Amazon Q Apps employees can go from conversation to generative AI-powered app based on their company data in seconds. Users simply describe the application they want in a prompt and Amazon Q instantly generates it. Amazon Q also gives employees the option to generate an app from an existing conversation with a single click. During preview, we saw users generate applications for diverse tasks, including summarizing feedback, creating onboarding plans, writing copy, drafting memos, and many more. For instance, Druva, a data security provider, created an Amazon Q App to support their request for proposal (RFP) process by summarizing the required information almost instantly, reducing RFP response times by up to 25%.
In addition to Amazon Q Apps, which makes it easy for any employee to automate their individual tasks, today we announced AWS App Studio (preview), a generative AI-powered service that enables technical professionals such as IT project managers, data engineers, and enterprise architects to use natural language to create, deploy, and manage enterprise applications across an organization. With App Studio, a user simply describes the application they want, what they want it to do, and the data sources they want to integrate with, and App Studio builds an application in minutes that could have taken a professional developer days to build a similar application from scratch. App Studio’s generative AI-powered assistant eliminates the learning curve of typical low-code tools, accelerating the application creation process and simplifying common tasks like designing the UI, building workflows, and testing the application. Each application can be immediately scaled to thousands of users and is secure and fully managed by AWS, eliminating the need for any operational expertise.
New features and capabilities supercharge Amazon Bedrock—speeding development of generative AI apps
Amazon Bedrock is the fastest and easiest way to build and scale secure generative AI applications with the broadest selection of leading LLMs and FMs as well as easy-to-use capabilities for developers. Tens of thousands of customers are already using Amazon Bedrock, and it’s one of AWS’s fastest growing services over the last decade. For example, Ferrari is rapidly introducing new experiences for customers, dealers, and internal teams to run faster simulations, create new knowledge bases that assist dealers and technical users, enhance the racing fan experience, and create hyper-personalized vehicle recommendations for customers from the millions of options offered by Ferrari in seconds.
Since the start of 2024, we have announced the general availability of more features and capabilities in Amazon Bedrock than comparable services from other leading cloud providers to help customers get generative AI apps from proof of concept to production faster. This includes support for new industry-leading models from Anthropic, Meta, Mistral, and more, as well as the recent addition of Anthropic Claude 3.5 Sonnet, their most advanced model to date, which was made available immediately for Amazon Bedrock customers. Thousands of customers have already used Anthropic’s Claude 3.5 since its release.
Today, we announced some major new Amazon Bedrock innovations that enable you to:
Customize generative AI applications with your data. You can customize generative AI applications with your data to make them specific to your use case, your organization, and your industry:
- Fine tune Anthropic’s Claude 3 Haiku in Amazon Bedrock – With Amazon Bedrock, you can privately and securely fine tune Amazon Titan, Cohere Command and Command Lite, and Meta Llama 2 models by providing labeled data in Amazon Simple Storage Service (Amazon S3) to specialize the model for your business and use case. Starting today, Amazon Bedrock is also the only fully managed service that provides you with the ability to fine tune Anthropic’s Claude 3 Haiku (in preview). Read more in the News Blog.
- Leverage even more data sources for Retrieval Augmented Generation (RAG) – With RAG, you can provide a model with new knowledge or up-to-date info from multiple sources, including document repositories, databases, and APIs. For example, the model might use RAG to retrieve search results from Amazon OpenSearch Service or documents from Amazon S3. Knowledge Bases for Amazon Bedrock fully manages this experience by connecting to your private data sources, including Amazon Aurora, Amazon OpenSearch Serverless, MongoDB, Pinecone, and Redis Enterprise Cloud. Today, we’ve expanded the list to include connectors for Salesforce, Confluence, and SharePoint (in preview), so organizations can leverage more business data to customize models for their specific needs. More knowledge base updates can be found in the News Blog.
- Get the fastest vector search available – To further enhance your RAG workflows, we’ve added vector search to some of our most popular data services, including OpenSearch Service and OpenSearch Serverless, Aurora, Amazon Relational Database Service (Amazon RDS), and more. Customers can co-locate vector data with operational data, reducing the overhead of managing another database. Today, we’re also excited to announce the general availability of vector search for Amazon MemoryDB. Amazon MemoryDB delivers the fastest vector search performance at the highest recall rates among popular vector databases on AWS, making it a great fit for use cases that require single-digit millisecond latency. For example, Amazon Advertising, IBISWorld, Mediaset, and other organizations are using it to deliver real-time semantic search, and Broadridge Financial is running RAG while delivering the same real-time response rates that their customers are accustomed to. You can use MemoryDB vector search standalone today, and soon, you’ll be able to access it through Knowledge Bases for Amazon Bedrock. Read more about MemoryDB in the News Blog.
Create more advanced, personalized customer experiences. With Agents for Amazon Bedrock, applications can take action, executing multistep tasks using company systems and data sources, making generative AI applications substantially more useful. Today, we’re adding key capabilities to Agents for Amazon Bedrock. Previously, agents were limited to taking action based on information from within a single session. Now agents can retain memory across multiple interactions to remember where you last left off and provide better recommendations based on prior interactions. For instance, in a flight booking application, a developer can create an agent that can remember the last time you traveled or that you opt for a vegetarian meal. Agents can also now interpret code to tackle complex data-driven use cases, such as data analysis, data visualization, text processing, solving equations, and optimization problems. For instance, an application user can ask to analyze the historical real estate prices across various zip codes to identify investment opportunities. Check out the News Blogs for more on these capabilities.
De-risk generative AI with Guardrails for Amazon Bedrock. Customers are concerned about hallucinations, where LLMs generate incorrect responses by conflating multiple pieces of information, providing incorrect information, or inventing new information. These results can misinform employees and customers and harm brands, limiting the usefulness of generative AI. Today, we’re adding contextual grounding checks in Guardrails for Amazon Bedrock to detect hallucinations in model responses for applications using RAG and summarization applications. Contextual grounding checks add to the industry-leading safety protection in Guardrails for Amazon Bedrock to make sure the LLM response is based on the right enterprise source data and evaluates the LLM response to confirm that it’s relevant to the user’s query or instruction. Contextual grounding checks can detect and filter over 75% hallucinated responses for RAG and summarization workloads. Read more about our commitments to responsible AI on the AWS Machine Learning Blog.
We’re excited to see how our customers leverage these ever-expanding capabilities of Amazon Bedrock to customize their generative AI applications for vertical industries and business functions. For example, Deloitte is using Amazon Bedrock’s advanced customization capabilities to build their C-Suite AI
solution, designed specifically for CFOs. It leverages Deloitte’s proprietary data and industry depth across the finance function. C-Suite AI provides customized AI models tailored to the needs of CFOs, with applications that span critical finance areas, generative analytics for data-driven insights, contract intelligence, and investor relations support.
New partners and trainings help customers along the AI journey
Our extensive partner network helps our customers along the journey to realizing the potential of generative AI. For example, BrainBox AI—which worked with our generative AI competency partner, Caylent—developed its AI assistant ARIA on AWS to help reduce energy costs and emissions in buildings. We have been building out our partner network and training offerings to help customers move quickly from experiment to broad usage. Our AWS Generative AI Competency Partner Program is designed to identify, validate, and promote AWS Partners with demonstrated AWS technical expertise and proven customer success. Today 19 new partners joined the program, giving customers access to 60 Generative AI Competency Partners across the globe. New partners include C3.ai, Cognizant, IBM, and LG CNS, and we have significantly expanded customer offerings into Korea, Greater China, and LATAM, and Saudi Arabia.
We’re also announcing a new partnership with Scale AI, our first model customization and evaluation partner. Through this collaboration, enterprise and public sector organizations can use Scale GenAI Platform and Scale Donovan to evaluate their generative AI applications and further customize, configure, and fine tune models to ensure trust and high performance in production, all built on Amazon Bedrock. Scale AI upholds the highest standards of privacy and regulatory compliance working with some of the most stringent government customers, such as the US Department of Defense. Customers can access Scale AI through an engagement with the AWS Generative AI Innovation Center, a program offered by AWS that pairs you with AWS science and strategy experts, or through the AWS Marketplace.
To help upskill your workforce, we’re making a new interactive online learning experience available, AWS SimuLearn, that pairs generative AI-powered simulations and hands-on training, to help people learn how to translate business problems into technical solutions. This is part of our broader commitment to provide free cloud computing skills training to 29 million people worldwide by 2025. Today, we announced that we surpassed this milestone, more than a year ahead of schedule.
We’re giving customers tools that put the power of generative AI into all employees’ hands, providing more ways to create personalized and relevant generative AI-powered applications, and working on the tough problems like reducing hallucinations so more companies can gain benefits from generative AI. We’re energized by the progress our customers have already made in making generative AI a reality for their organizations and will continue to innovate on their behalf. To watch the New York Summit keynote for an in-depth look at these announcements, visit our AWS New York Summit page or learn more about our generative AI services.
About the author
Swami Sivasubramanian is Vice President of Data and Machine Learning at AWS. In this role, Swami oversees all AWS Database, Analytics, and AI & Machine Learning services. His team’s mission is to help organizations put their data to work with a complete, end-to-end data solution to store, access, analyze, and visualize, and predict.