Machine Learning

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

AI-powered speech solutions are transforming contact centers by enabling natural conversations between customers and AI agents, shortening wait times, and dramatically reducing operational costs—all without sacrificing the human-like interaction customers expect. With the recent launch of Amazon Nova Sonic in Amazon Bedrock, you can now build sophisticated conversational AI agents that communicate naturally through voice, … Read more

Successful generative AI software as a service (SaaS) systems require a balance between service scalability and cost management. This becomes critical when building a multi-tenant generative AI service designed to serve a large, diverse customer base while maintaining rigorous cost controls and comprehensive usage monitoring. Traditional cost management approaches for such systems often reveal limitations. … Read more

Evaluating the performance of large language models (LLMs) goes beyond statistical metrics like perplexity or bilingual evaluation understudy (BLEU) scores. For most real-world generative AI scenarios, it’s crucial to understand whether a model is producing better outputs than a baseline or an earlier iteration. This is especially important for applications such as summarization, content generation, … Read more

Vector embeddings have become essential for modern Retrieval Augmented Generation (RAG) applications, but organizations face significant cost challenges as they scale. As knowledge bases grow and require more granular embeddings, many vector databases that rely on high-performance storage such as SSDs or in-memory solutions become prohibitively expensive. This cost barrier often forces organizations to limit … Read more

Amazon Bedrock offers model customization capabilities for customers to tailor versions of foundation models (FMs) to their specific needs through features such as fine-tuning and distillation. Today, we’re announcing the launch of on-demand deployment for customized models ready to be deployed on Amazon Bedrock. On-demand deployment for customized models provides an additional deployment option that … Read more

Organizations are adopting large language models (LLMs), such as DeepSeek R1, to transform business processes, enhance customer experiences, and drive innovation at unprecedented speed. However, standalone LLMs have key limitations such as hallucinations, outdated knowledge, and no access to proprietary data. Retrieval Augmented Generation (RAG) addresses these gaps by combining semantic search with generative AI, … Read more

This post was written with Ilan Geller, Kamal Mannar, Debasmita Ghosh, and Nakul Aggarwal of Accenture. Video highlights offer a powerful way to boost audience engagement and extend content value for content publishers. These short, high-impact clips capture key moments that drive viewer retention, amplify reach across social media, reinforce brand identity, and open new … Read more

This post is co-written with Mark Berkeland, Oscar Rodriguez and Marina Gerzon from Vonage. Voice-based technologies are transforming the way businesses engage with customers across customer support, virtual assistants, and intelligent agents. However, creating real-time, expressive, and highly responsive voice interfaces still requires navigating a complex stack of communication protocols, AI models, and media infrastructure. … Read more

AI agents will change how we all work and live. Our AWS CEO, Matt Garman, shared a vision of a technological shift as transformative as the advent of the internet. I’m energized by this vision because I’ve witnessed firsthand how these intelligent agent systems are already beginning to solve complex problems, automate workflows, and create … Read more

Amazon Bedrock Knowledge Bases has extended its vector store options by enabling support for Amazon OpenSearch Service managed clusters, further strengthening its capabilities as a fully managed Retrieval Augmented Generation (RAG) solution. This enhancement builds on the core functionality of Amazon Bedrock Knowledge Bases , which is designed to seamlessly connect foundation models (FMs) with … Read more

This post was co-written with Mohammad Jama, Yun Kim, and Barry Eom from Datadog. The emergence of generative AI agents in recent years has transformed the AI landscape, driven by advances in large language models (LLMs) and natural language processing (NLP). The focus is shifting from simple AI assistants to Agentic AI systems that can … Read more

This is a guest post co-written with Rahul Ghosh, Sandeep Kumar Veerlapati, Rahmat Khan, and Mudit Chopra from PayU. PayU offers a full-stack digital financial services system that serves the financial needs of merchants, banks, and consumers through technology. As a Central Bank-regulated financial institution in India, we recently observed a surge in our employees’ … Read more

This post is co-written with Andrew Liu, Chelsea Isaac, Zoey Zhang, and Charlie Huang from NVIDIA. DGX Cloud on Amazon Web Services (AWS) represents a significant leap forward in democratizing access to high-performance AI infrastructure. By combining NVIDIA GPU expertise with AWS scalable cloud services, organizations can accelerate their time-to-train, reduce operational complexity, and unlock … Read more

This post is co-written with Kshitiz Gupta, Wenhan Tan, Arun Raman, Jiahong Liu, and Eiluth Triana Isaza from NVIDIA. As large language models (LLMs) and generative AI applications become increasingly prevalent, the demand for efficient, scalable, and low-latency inference solutions has grown. Traditional inference systems often struggle to meet these demands, especially in distributed, multi-node … Read more

When we launched the AWS Generative AI Innovation Center in 2023, we had one clear goal: help customers turn AI potential into real business value. We’ve already guided thousands of customers across industries from financial services to healthcare—including Formula 1, FOX, GovTech Singapore, Itaú Unibanco, Nasdaq, NFL, RyanAir, and S&P Global—from AI experimentation to full-scale … Read more

Vehicle data is critical for original equipment manufacturers (OEMs) to drive continuous product innovation and performance improvements and to support new value-added services. Similarly, the increasing digitalization of vehicle architectures and adoption of software-configurable functions allow OEMs to add new features and capabilities efficiently. Sonatus’s Collector AI and Automator AI products address these two aspects … Read more

This post is cowritten with Jimmy Cancilla from Rapid7. Organizations are managing increasingly distributed systems, which span on-premises infrastructure, cloud services, and edge devices. As systems become interconnected and exchange data, the potential pathways for exploitation multiply, and vulnerability management becomes critical to managing risk. Vulnerability management (VM) is the process of identifying, classifying, prioritizing, … Read more

Data is your generative AI differentiator, and successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Traditional data architectures often struggle to meet the unique demands of generative such as applications. An effective generative AI data strategy requires several key components like seamless integration of diverse data sources, … Read more