Implement human-in-the-loop confirmation with Amazon Bedrock Agents
Agents are revolutionizing how businesses automate complex workflows and decision-making processes. Amazon Bedrock Agents helps you accelerate generative AI application development by orchestrating multi-step tasks. Agents use the reasoning capability of foundation models (FMs) to break down user-requested tasks into multiple steps. In addition, they use the developer-provided instruction to create an orchestration plan and […]
Boost team productivity with Amazon Q Business Insights
Employee productivity is a critical factor in maintaining a competitive advantage. Amazon Q Business offers a unique opportunity to enhance workforce efficiency by providing AI-powered assistance that can significantly reduce the time spent searching for information, generating content, and completing routine tasks. Amazon Q Business is a fully managed, generative AI-powered assistant that lets you […]
Multi-LLM routing strategies for generative AI applications on AWS
Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. Although an individual LLM can be highly capable, it might not optimally address a wide range of use cases or meet diverse performance requirements. The multi-LLM approach enables organizations to effectively choose the right model for each task, adapt to different […]
How iFood built a platform to run hundreds of machine learning models with Amazon SageMaker Inference
Headquartered in São Paulo, Brazil, iFood is a national private company and the leader in food-tech in Latin America, processing millions of orders monthly. iFood has stood out for its strategy of incorporating cutting-edge technology into its operations. With the support of AWS, iFood has developed a robust machine learning (ML) inference infrastructure, using services […]
Repurposing Protein Folding Models for Generation with Latent Diffusion
PLAID is a multimodal generative model that simultaneously generates protein 1D sequence and 3D structure, by learning the latent space of protein folding models. The awarding of the 2024 Nobel Prize to AlphaFold2 marks an important moment of recognition for the of AI role in biology. What comes next after protein folding? In PLAID, we […]
Build an enterprise synthetic data strategy using Amazon Bedrock
The AI landscape is rapidly evolving, and more organizations are recognizing the power of synthetic data to drive innovation. However, enterprises looking to use AI face a major roadblock: how to safely use sensitive data. Stringent privacy regulations make it risky to use such data, even with robust anonymization. Advanced analytics can potentially uncover hidden […]
Llama 4 family of models from Meta are now available in SageMaker JumpStart
Today, we’re excited to announce the availability of Llama 4 Scout and Maverick models in Amazon SageMaker JumpStart and coming soon in Amazon Bedrock. Llama 4 represents Meta’s most advanced multimodal models to date, featuring a mixture of experts (MoE) architecture and context window support up to 10 million tokens. With native multimodality and early fusion […]
Multi-tenancy in RAG applications in a single Amazon Bedrock knowledge base with metadata filtering
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies and AWS. Amazon Bedrock Knowledge Bases offers fully managed, end-to-end Retrieval Augmented Generation (RAG) workflows to create highly accurate, low-latency, secure, and custom generative AI applications by incorporating contextual information from your company’s data sources. […]
Effectively use prompt caching on Amazon Bedrock
Prompt caching, now generally available on Amazon Bedrock with Anthropic’s Claude 3.5 Haiku and Claude 3.7 Sonnet, along with Nova Micro, Nova Lite, and Nova Pro models, lowers response latency by up to 85% and reduces costs up to 90% by caching frequently used prompts across multiple API calls. With prompt caching, you can mark […]
Advanced tracing and evaluation of generative AI agents using LangChain and Amazon SageMaker AI MLFlow
Developing generative AI agents that can tackle real-world tasks is complex, and building production-grade agentic applications requires integrating agents with additional tools such as user interfaces, evaluation frameworks, and continuous improvement mechanisms. Developers often find themselves grappling with unpredictable behaviors, intricate workflows, and a web of complex interactions. The experimentation phase for agents is particularly […]