Machine Learning

IT teams face mounting challenges as they manage increasingly complex infrastructure and applications, often spending countless hours manually identifying operational issues, troubleshooting problems, and performing repetitive maintenance tasks. This operational burden diverts valuable technical resources from innovation and strategic initiatives. Artificial intelligence for IT operations (AIOps) presents a transformative solution, using AI to automate operational … Read more

Organizations can optimize their migration and modernization projects by streamlining the containerization process for legacy applications. With the right tools and approaches, teams can transform traditional applications into containerized solutions efficiently, reducing the time spent on manual coding, testing, and debugging while enhancing developer productivity and accelerating time-to-market. During containerization initiatives, organizations can address compatibility, … Read more

Picture this: your machine learning (ML) team has a promising model to train and experiments to run for their generative AI project, but they’re waiting for GPU availability. The ML scientists spend time monitoring instance availability, coordinating with teammates over shared resources, and managing infrastructure allocation. Simultaneously, your infrastructure administrators spend significant time trying to … Read more

Developers building AI applications face a common challenge: converting unstructured data into structured formats. Structured output is critical for machine-to-machine communication use cases, because this enables downstream use cases to more effectively consume and process the generated outputs. Whether it’s extracting information from documents, creating assistants that fetch data from APIs, or developing agents that … Read more

As organizations seek to derive greater value from their AWS Support data, operational teams are looking for ways to transform raw support cases and health events into actionable insights. While traditional analytics tools can provide basic reporting capabilities, teams need more sophisticated solutions that can understand and process natural language queries about their operational data. … Read more

The Amazon Strands Agents SDK is an open source framework for building AI agents that emphasizes a model-driven approach. Instead of hardcoding complex task flows, Strands uses the reasoning abilities of modern large language models (LLMs) to handle planning and tool usage autonomously. Developers can create an agent with a prompt (defining the agent’s role … Read more

“Tavily is now available on AWS Marketplace and integrates natively with Amazon Bedrock AgentCore Gateway. This makes it even faster for developers and enterprises to embed real-time web intelligence into secure, AWS-powered agents.” As enterprises accelerate their AI adoption, the demand for agent frameworks that can autonomously gather, process, and synthesize information has increased. Traditional … Read more

Organizations across various sectors face significant challenges when converting meeting recordings or recorded presentations into structured documentation. The process of creating handouts from presentations requires lots of manual effort, such as reviewing recordings to identify slide transitions, transcribing spoken content, capturing and organizing screenshots, synchronizing visual elements with speaker notes, and formatting content. These challenges … Read more

Customers are increasingly looking to use the power of large language models (LLMs) to solve real-world problems. However, bridging the gap between these LLMs and practical applications has been a challenge. AI agents have appeared as an innovative technology that bridges this gap. The foundation models (FMs) available through Amazon Bedrock serve as the cognitive … Read more

Today, we’re excited to announce that Mistral-Small-3.2-24B-Instruct-2506—a 24-billion-parameter large language model (LLM) from Mistral AI that’s optimized for enhanced instruction following and reduced repetition errors—is available for customers through Amazon SageMaker JumpStart and Amazon Bedrock Marketplace. Amazon Bedrock Marketplace is a capability in Amazon Bedrock that developers can use to discover, test, and use over … Read more

Financial regulations and compliance are constantly changing, and automation of compliance reporting has emerged as a game changer in the financial industry. Amazon Web Services (AWS) generative AI solutions offer a seamless and efficient approach to automate this reporting process. The integration of AWS generative AI into the compliance framework not only enhances efficiency but … Read more

Fine-tuning of large language models (LLMs) has emerged as a crucial technique for organizations seeking to adapt powerful foundation models (FMs) to their specific needs. Rather than training models from scratch—a process that can cost millions of dollars and require extensive computational resources—companies can customize existing models with domain-specific data at a fraction of the … Read more

This post is co-written with Abhinav Pandey from Nippon Life India Asset Management Ltd. Accurate information retrieval through generative AI-powered assistants is a popular use case for enterprises. To reduce hallucination and improve overall accuracy, Retrieval Augmented Generation (RAG) remains the most commonly used method to retrieve reliable and accurate responses that use enterprise data … Read more

Drug discovery is a complex, time-intensive process that requires researchers to navigate vast amounts of scientific literature, clinical trial data, and molecular databases. Life science customers like Genentech and AstraZeneca are using AI agents and other generative AI tools to increase the speed of scientific discovery. Builders at these organizations are already using the fully … Read more

In early 2025, we introduced the Amazon Nova Act SDK as a research preview to help developers build agents that reliably complete tasks in a web browser. Now, we are excited to work with customers to take their agents to production in a limited preview, using new AWS integrations including AWS Identity and Access Management … Read more

This post is co-written with Jessie Jiao from Crypto.com. Crypto.com is a crypto exchange and comprehensive trading service serving 140 million users in 90 countries. To improve the service quality of Crypto.com, the firm implemented generative AI-powered assistant services on AWS. Modern AI assistants—artificial intelligence systems designed to interact with users through natural language, answer … Read more

Building modern serverless applications on AWS requires navigating best practices to manage the integration between multiple services, such as AWS Lambda, Amazon API Gateway, Amazon DynamoDB, and Amazon EventBridge. Security considerations, performance optimization, and implementing a comprehensive monitoring systems adds further requirements to build a serverless architecture while adhering to AWS best practices. Amazon Q Developer CLI with Model Context Protocol … Read more

Legal teams spend bulk of their time manually reviewing documents during eDiscovery. This process involves analyzing electronically stored information across emails, contracts, financial records, and collaboration systems for legal proceedings. This manual approach creates significant bottlenecks: attorneys must identify privileged communications, assess legal risks, extract contractual obligations, and maintain regulatory compliance across thousands of documents … Read more

This post is co-written with Bogdan Arsenie and Nick Mattei from PerformLine. PerformLine operates within the marketing compliance industry, a specialized subset of the broader compliance software market, which includes various compliance solutions like anti-money laundering (AML), know your customer (KYC), and others. Specifically, marketing compliance refers to adhering to regulations and guidelines set by … Read more

Cold start in recommendation systems goes beyond just new user or new item problems—it’s the complete absence of personalized signals at launch. When someone first arrives, or when fresh content appears, there’s no behavioral history to tell the engine what they care about, so everyone ends up in broad generic segments. That not only dampens … Read more

Large language models (LLMs) have rapidly evolved, becoming integral to applications ranging from conversational AI to complex reasoning tasks. However, as models grow in size and capability, effectively evaluating their performance has become increasingly challenging. Traditional benchmarking metrics like perplexity and BLEU scores often fail to capture the nuances of real-world interactions, making human-aligned evaluation … Read more

At the AWS Summit in New York City, we introduced a comprehensive suite of model customization capabilities for Amazon Nova foundation models. Available as ready-to-use recipes on Amazon SageMaker AI, you can use them to adapt Nova Micro, Nova Lite, and Nova Pro across the model training lifecycle, including pre-training, supervised fine-tuning, and alignment. In this … Read more

In recent years, the emergence of large language models (LLMs) has accelerated AI adoption across various industries. However, to further augment LLMs’ capabilities and effectively use up-to-date information and domain-specific knowledge, integration with external data sources is essential. Retrieval Augmented Generation (RAG) has gained attention as an effective approach to address this challenge. RAG is … Read more

Today’s enterprises increasingly rely on AI-driven applications to enhance decision-making, streamline workflows, and deliver improved customer experiences. Achieving these outcomes demands secure, timely, and accurate access to authoritative data—especially when such data resides across diverse repositories and applications within strict enterprise security boundaries. Interoperable technologies powered by open standards like the Model Context Protocol (MCP) … Read more

In 2024, the Ministry of Economy, Trade and Industry (METI) launched the Generative AI Accelerator Challenge (GENIAC)—a Japanese national program to boost generative AI by providing companies with funding, mentorship, and massive compute resources for foundation model (FM) development. AWS was selected as the cloud provider for GENIAC’s second cycle (cycle 2). It provided infrastructure … Read more

Data science teams working with artificial intelligence and machine learning (AI/ML) face a growing challenge as models become more complex. While Amazon Deep Learning Containers (DLCs) offer robust baseline environments out-of-the-box, customizing them for specific projects often requires significant time and expertise. In this post, we explore how to use Amazon Q Developer and Model … Read more

Extracting meaningful insights from unstructured data presents significant challenges for many organizations. Meeting recordings, customer interactions, and interviews contain invaluable business intelligence that remains largely inaccessible due to the prohibitive time and resource costs of manual review. Organizations frequently struggle to efficiently capture and use key information from these interactions, resulting in not only productivity … Read more

This post was written with Zach Heath of Kyruus Health. When health plan members need care, they shouldn’t need a dictionary. Yet millions face this exact challenge—describing symptoms in everyday language while healthcare references clinical terminology and complex specialty classifications. This disconnect forces members to become amateur medical translators, attempting to convert phrases like “my … Read more

In the manufacturing world, valuable insights from service reports often remain underutilized in document storage systems. This post explores how Amazon Web Services (AWS) customers can build a solution that automates the digitisation and extraction of crucial information from many reports using generative AI. The solution uses Amazon Nova Pro on Amazon Bedrock and Amazon … Read more

Generative AI is transforming how businesses deliver personalized experiences across industries, including travel and hospitality. Travel agents are enhancing their services by offering personalized holiday packages, carefully curated for customer’s unique preferences, including accessibility needs, dietary restrictions, and activity interests. Meeting these expectations requires a solution that combines comprehensive travel knowledge with real-time pricing and … Read more