AWS Machine Learning Blog
Tag: Generative AI
How Formula 1® uses generative AI to accelerate race-day issue resolution
In this post, we explain how F1 and AWS have developed a root cause analysis (RCA) assistant powered by Amazon Bedrock to reduce manual intervention and accelerate the resolution of recurrent operational issues during races from weeks to minutes. The RCA assistant enables the F1 team to spend more time on innovation and improving its services, ultimately delivering an exceptional experience for fans and partners. The successful collaboration between F1 and AWS showcases the transformative potential of generative AI in empowering teams to accomplish more in less time.
Build a dynamic, role-based AI agent using Amazon Bedrock inline agents
In this post, we explore how to build an application using Amazon Bedrock inline agents, demonstrating how a single AI assistant can adapt its capabilities dynamically based on user roles.
From concept to reality: Navigating the Journey of RAG from proof of concept to production
In this post, we explore the movement of RAG applications from their proof of concept or minimal viable product (MVP) phase to full-fledged production systems. When transitioning a RAG application from a proof of concept to a production-ready system, optimization becomes crucial to make sure the solution is reliable, cost-effective, and high-performing.
Falcon 3 models now available in Amazon SageMaker JumpStart
We are excited to announce that the Falcon 3 family of models from TII are available in Amazon SageMaker JumpStart. In this post, we explore how to deploy this model efficiently on Amazon SageMaker AI.
Amazon Q Business simplifies integration of enterprise knowledge bases at scale
In this post, we demonstrate how to build a knowledge base solution by integrating enterprise data with Amazon Q Business using Amazon S3. This approach helps organizations improve operational efficiency, reduce response times, and gain valuable insights from their historical data. The solution uses AWS security best practices to promote data protection while enabling teams to create a comprehensive knowledge base from various data sources.
How Aetion is using generative AI and Amazon Bedrock to translate scientific intent to results
Aetion is a leading provider of decision-grade real-world evidence software to biopharma, payors, and regulatory agencies. In this post, we review how Aetion is using Amazon Bedrock to help streamline the analytical process toward producing decision-grade real-world evidence and enable users without data science expertise to interact with complex real-world datasets.
Trellix lowers cost, increases speed, and adds delivery flexibility with cost-effective and performant Amazon Nova Micro and Amazon Nova Lite models
This post discusses the adoption and evaluation of Amazon Nova foundation models by Trellix, a leading company delivering cybersecurity’s broadest AI-powered platform to over 53,000 customers worldwide.
Build a multi-interface AI assistant using Amazon Q and Slack with Amazon CloudFront clickable references from an Amazon S3 bucket
There is consistent customer feedback that AI assistants are the most useful when users can interface with them within the productivity tools they already use on a daily basis, to avoid switching applications and context. Web applications like Amazon Q Business and Slack have become essential environments for modern AI assistant deployment. This post explores how diverse interfaces enhance user interaction, improve accessibility, and cater to varying preferences.
Orchestrate seamless business systems integrations using Amazon Bedrock Agents
The post showcases how generative AI can be used to logic, reason, and orchestrate integrations using a fictitious business process. It demonstrates strategies and techniques for orchestrating Amazon Bedrock agents and action groups to seamlessly integrate generative AI with existing business systems, enabling efficient data access and unlocking the full potential of generative AI.
How Aetion is using generative AI and Amazon Bedrock to unlock hidden insights about patient populations
In this post, we review how Aetion’s Smart Subgroups Interpreter enables users to interact with Smart Subgroups using natural language queries. Powered by Amazon Bedrock and Anthropic’s Claude 3 large language models (LLMs), the interpreter responds to user questions expressed in conversational language about patient subgroups and provides insights to generate further hypotheses and evidence.