​[[{“value”:” Large language models (LLMs) have become increasingly prevalent across both consumer and enterprise applications. However, their tendency to “hallucinate” information and deliver incorrect answers with seeming confidence has created a trust problem. Think of LLMs as you would a human expert: we typically trust… Read More »Citations with Amazon Nova understanding models Sunita Koppar Artificial Intelligence

​Visualizing model performance is an essential piece of the machine learning workflow puzzle. Visualizing model performance is an essential piece of the machine learning workflow puzzle.  Read More  

​[[{“value”:” If you want to make the most of The Field We Now Call AI, look to trading. Specifically, the tech-driven sort. People who’ve read my other work, or who have had the misfortune of speaking with me one-on-one, have already heard this line. My… Read More »Taming the Delightful Chaos Q McCallum AI & ML – Radar

​[[{“value”:” Organizations are increasingly excited about the potential of AI agents, but many find themselves stuck in what we call “proof of concept purgatory”—where promising agent prototypes struggle to make the leap to production deployment. In our conversations with customers, we’ve heard consistent challenges that… Read More »Securely launch and scale your agents and tools on Amazon Bedrock AgentCore Runtime Shreyas Subramanian Artificial Intelligence

​[[{“value”:” This is a guest post co-written with Scott Likens, Ambuj Gupta, Adam Hood, Chantal Hudson, Priyanka Mukhopadhyay, Deniz Konak Ozturk, and Kevin Paul from PwC Organizations are deploying generative AI solutions while balancing accuracy, security, and compliance. In this globally competitive environment, scale matters… Read More »PwC and AWS Build Responsible AI with Automated Reasoning on Amazon Bedrock Nafi Diallo Artificial Intelligence

​[[{“value”:” At Amazon, our team builds Rufus, a generative AI-powered shopping assistant that serves millions of customers at immense scale. However, deploying Rufus at scale introduces significant challenges that must be carefully navigated. Rufus is powered by a custom-built large language model (LLM). As the… Read More »How Amazon scaled Rufus by building multi-node inference using AWS Trainium chips and vLLM James Park Artificial Intelligence

​[[{“value”:” Agentic AI is revolutionizing the financial services industry through its ability to make autonomous decisions and adapt in real time, moving well beyond traditional automation. Imagine an AI assistant that can analyze quarterly earnings reports, compare them against industry expectations, and generate insights about… Read More »Build an intelligent financial analysis agent with LangGraph and Strands Agents Evan Grenda Artificial Intelligence

​[[{“value”:” AI assistants that forget what you told them 5 minutes ago aren’t very helpful. While large language models (LLMs) excel at generating human-like responses, they are fundamentally stateless—they don’t retain information between interactions. This forces developers to build custom memory systems to track conversation… Read More »Amazon Bedrock AgentCore Memory: Building context-aware agents Akarsha Sehwag Artificial Intelligence

​[[{“value”:” Data analysis often presents significant challenges for business users who aren’t proficient in SQL. Traditional methods require technical expertise to query databases, leading to delayed insights and dependence on data teams. Many organizations struggle with making their data accessible to business users while maintaining… Read More »Build a conversational natural language interface for Amazon Athena queries using Amazon Nova Ravi Kumar Artificial Intelligence