Machine Learning

The MERN (MongoDB, Express, React, Node.js) stack is a popular JavaScript web development framework. The combination of technologies is well-suited for building scalable, modern web applications, especially those requiring real-time updates and dynamic user interfaces. Amazon Q Developer is a generative AI-powered assistant that improves developer efficiency across the different phases of the software development … Read more

This post is a joint collaboration between Salesforce and AWS and is being cross-published on both the Salesforce Engineering Blog and the AWS Machine Learning Blog. The Salesforce AI Platform Model Serving team is dedicated to developing and managing services that power large language models (LLMs) and other AI workloads within Salesforce. Their main focus … Read more

Chat-based assistants powered by Retrieval Augmented Generation (RAG) are transforming customer support, internal help desks, and enterprise search, by delivering fast, accurate answers grounded in your own data. With RAG, you can use a ready-to-deploy foundation model (FM) and enrich it with your own data, making responses relevant and context-aware without the need for fine-tuning … Read more

We’re excited to introduce Amazon Bedrock AgentCore Identity, a comprehensive identity and access management service purpose-built for AI agents. With AgentCore Identity AI, agent developers and administrators can securely access AWS resources and third-party tools such as GitHub, Salesforce, or Slack. AgentCore Identity provides robust identity and access management at scale so that agents can … Read more

Intelligent document processing (IDP) is a technology to automate the extraction, analysis, and interpretation of critical information from a wide range of documents. By using advanced machine learning (ML) and natural language processing algorithms, IDP solutions can efficiently extract and process structured data from unstructured text, streamlining document-centric workflows. When enhanced with generative AI capabilities, … Read more

Upgrading legacy systems has become increasingly important to stay competitive in today’s market as outdated infrastructure can cost organizations time, money, and market position. However, modernization efforts face challenges like time-consuming architecture reviews, complex migrations, and fragmented systems. These delays not only impact engineering teams but have broader impacts including lost market opportunities, reduced competitiveness, … Read more

Amazon Q Business is a generative AI-powered enterprise assistant that helps organizations unlock value from their data. By connecting to enterprise data sources, employees can use Amazon Q Business to quickly find answers, generate content, and automate tasks—from accessing HR policies to streamlining IT support workflows, all while respecting existing permissions and providing clear citations. … Read more

This post was co-authored with Laura Lee Williams and John Jabara from University Startups. University Startups, headquartered in Bethesda, MD, was founded in 2020 to empower high school students to expand their education beyond a traditional curriculum. University Startups is focused on special education and related services in school districts throughout the US. After students … Read more

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 experts who can back up their claims with references and walk … Read more

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 block the path from experimentation to enterprise-grade deployment: “Our developers want … Read more

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 less, speed matters more, and innovation matters most of all, according … Read more

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 model’s complexity increased, we prioritized developing scalable multi-node inference capabilities that … Read more

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 future performance. This seemingly straightforward task involves multiple complex steps: document … Read more

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 history, remember user preferences, and maintain context across sessions, often solving … Read more

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 the analytical capabilities of Amazon Athena. Modern AI agents are transforming … Read more

Imagine harnessing the power of 72 cutting-edge NVIDIA Blackwell GPUs in a single system for the next wave of AI innovation, unlocking 360 petaflops of dense 8-bit floating point (FP8) compute and 1.4 exaflops of sparse 4-bit floating point (FP4) compute. Today, that’s exactly what Amazon SageMaker HyperPod delivers with the launch of support for … Read more

This post is co-written with Rudra Kannemadugu and Shravan K S from Indegene Limited. In today’s digital-first world, healthcare conversations are increasingly happening online. Yet the life sciences industry has struggled to keep pace with this shift, facing challenges in effectively analyzing and deriving insights from complex medical discussions on a scale. This post will … Read more

This post is co-authored with Karsten Weber and Rosary Wang from Lexbe. Legal professionals are frequently tasked with sifting through vast volumes of documents to identify critical evidence for litigation. This process can be time-consuming, prone to human error, and expensive—especially when tight deadlines loom. Lexbe, a leader in legal document review software, confronted these … Read more

In Part 1 of our series, we established the architectural foundation for an enterprise artificial intelligence and machine learning (AI/ML) configuration with Amazon SageMaker Unified Studio projects. We explored the multi-account structure, project organization, multi-tenancy approaches, and repository strategies needed to create a governed AI development environment. In this post, we focus on implementing this … Read more

Amazon SageMaker Unified Studio represents the evolution towards unifying the entire data, analytics, and artificial intelligence and machine learning (AI/ML) lifecycle within a single, governed environment. As organizations adopt SageMaker Unified Studio to unify their data, analytics, and AI workflows, they encounter new challenges around scaling, automation, isolation, multi-tenancy, and continuous integration and delivery (CI/CD). … Read more

“How much will it cost to run our chatbot on Amazon Bedrock?” This is one of the most frequent questions we hear from customers exploring AI solutions. And it’s no wonder — calculating costs for AI applications can feel like navigating a complex maze of tokens, embeddings, and various pricing models. Whether you’re a solution … Read more

Released on August 5, 2025, OpenAI’s GPT-OSS models, gpt-oss-20b and gpt-oss-120b, are now available on AWS through Amazon SageMaker AI and Amazon Bedrock. These pre-trained, text-only Transformer models are built on a Mixture-of-Experts (MoE) architecture that activates only a subset of parameters per token, delivering high reasoning performance while reducing compute costs. They specialize in … Read more

DTDC is India’s leading integrated express logistics provider, operating the largest network of customer access points in the country. DTDC’s technology-driven logistics solutions cater to a wide range of customers across diverse industry verticals, making them a trusted partner in delivering excellence. DTDC Express Limited receives over 400,000 customer queries each month, ranging from tracking … Read more

AI agents are rapidly transforming enterprise operations. Although a single agent can perform specific tasks effectively, complex business processes often span multiple systems, requiring data retrieval, analysis, decision-making, and action execution across different systems. With multi-agent collaboration, specialized AI agents can work together to automate intricate workflows. This post explores a practical collaboration, integrating Salesforce … Read more

At AWS, our sales teams create customer-focused documents called account plans to deeply understand each AWS customer’s unique goals and challenges, helping account teams provide tailored guidance and support that accelerates customer success on AWS. As our business has expanded, the account planning process has become more intricate, requiring detailed analysis, reviews, and cross-team alignment … Read more

Organizations today face a critical challenge: managing an ever-increasing volume of tasks and information across multiple systems. Although traditional task management tools help organize work, they often fall short in delivering the intelligence needed for truly efficient operations. Today, we’re excited to announce the integration of Asana AI Studio with Amazon Q index, bringing generative … Read more

The payments industry stands at the forefront of digital transformation, with artificial intelligence (AI) rapidly becoming a cornerstone technology that powers a variety of solutions, from fraud detection to customer service. According to the following Number Analytics report, digital payment transactions are projected to exceed $15 trillion globally by 2027. Generative AI has expanded the … Read more

In Part 1 of our series, we explored the foundational concepts of responsible AI in the payments industry. In this post, we discuss the practical implementation of responsible AI frameworks. The need for responsible AI The implementation of responsible AI is not passive, but a dynamic process of reimagining how technology can serve a customer’s … Read more

Organizations across industries face challenges with high volumes of multi-page documents that require intelligent processing to extract accurate information. Although automation has improved this process, human expertise is still needed in specific scenarios to verify data accuracy and quality. In March 2025, AWS launched Amazon Bedrock Data Automation, which enables developers to automate the generation … Read more

Organizations need user-friendly ways to build AI assistants that can reference enterprise documents while maintaining document security. This post shows how to use Amazon Q Business to create an AI assistant that provides clickable URLs to source documents stored in Amazon Simple Storage Service (Amazon S3), to support secure document access and verification. Amazon Q … Read more