AWSML

Amazon Bedrock announces the preview launch of Session Management APIs, a new capability that enables developers to simplify state and context management for generative AI applications built with popular open source frameworks such as LangGraph and LlamaIndex. Session Management APIs provide an out-of-the-box solution that enables developers to securely manage state and conversation context across … Read more

Deploying models efficiently, reliably, and cost-effectively is a critical challenge for organizations of all sizes. As organizations increasingly deploy foundation models (FMs) and other machine learning (ML) models to production, they face challenges related to resource utilization, cost-efficiency, and maintaining high availability during updates. Amazon SageMaker AI introduced inference component functionality that can help organizations … Read more

Amazon Bedrock Knowledge Bases is a fully managed capability that helps implement entire Retrieval Augmented Generation (RAG) workflows from ingestion to retrieval and prompt augmentation without having to build custom integrations to data sources and manage data flows. There is no single way to optimize knowledge base performance: each use case is impacted differently by … Read more

Since Amazon Q Business became generally available in 2024, customers have used this fully managed, generative AI-powered assistant to enhance their productivity and efficiency. The assistant enables users to answer questions, generate summaries, create content, and complete tasks using enterprise data. Today’s workforce faces significant application overload. According to Gartner, the average desk worker now … Read more

Today’s organizations face a critical challenge with the fragmentation of vital information across multiple environments. As businesses increasingly rely on diverse project management and IT service management (ITSM) tools such as ServiceNow, Atlassian Jira and Confluence, employees find themselves navigating a complex web of systems to access crucial data. This isolated approach leads to several … Read more

Research papers and engineering documents often contain a wealth of information in the form of mathematical formulas, charts, and graphs. Navigating these unstructured documents to find relevant information can be a tedious and time-consuming task, especially when dealing with large volumes of data. However, by using Anthropic’s Claude on Amazon Bedrock, researchers and engineers can … Read more

IT operations teams face the challenge of providing smooth functioning of critical systems while managing a high volume of incidents filed by end-users. Manual intervention in incident management can be time-consuming and error prone because it relies on repetitive tasks, human judgment, and potential communication gaps. Using generative AI for IT operations offers a transformative … Read more

As AWS environments grow in complexity, troubleshooting issues with resources can become a daunting task. Manually investigating and resolving problems can be time-consuming and error-prone, especially when dealing with intricate systems. Fortunately, AWS provides a powerful tool called AWS Support Automation Workflows, which is a collection of curated AWS Systems Manager self-service automation runbooks. These … Read more

Today we are announcing that general availability of Amazon Bedrock in Amazon SageMaker Unified Studio. Companies of all sizes face mounting pressure to operate efficiently as they manage growing volumes of data, systems, and customer interactions. Manual processes and fragmented information sources can create bottlenecks and slow decision-making, limiting teams from focusing on higher-value work. … Read more

Asure, a company of over 600 employees, is a leading provider of cloud-based workforce management solutions designed to help small and midsized businesses streamline payroll and human resources (HR) operations and ensure compliance. Their offerings include a comprehensive suite of human capital management (HCM) solutions for payroll and tax, HR compliance services, time tracking, 401(k) … Read more

Gartner predicts that “by 2027, 40% of generative AI solutions will be multimodal (text, image, audio and video) by 2027, up from 1% in 2023.” The McKinsey 2023 State of AI Report identifies data management as a major obstacle to AI adoption and scaling. Enterprises generate massive volumes of unstructured data, from legal contracts to customer … Read more

In many generative AI applications, a large language model (LLM) like Amazon Nova is used to respond to a user query based on the model’s own knowledge or context that it is provided. However, as use cases have matured, the ability for a model to have access to tools or structures that would be inherently … Read more

Generative AI is rapidly transforming the modern workplace, offering unprecedented capabilities that augment how we interact with text and data. At Amazon Web Services (AWS), we recognize that many of our customers rely on the familiar Microsoft Office suite of applications, including Word, Excel, and Outlook, as the backbone of their daily workflows. In this … Read more

As we gather for NVIDIA GTC, organizations of all sizes are at a pivotal moment in their AI journey. The question is no longer whether to adopt generative AI, but how to move from promising pilots to production-ready systems that deliver real business value. The organizations that figure this out first will have a significant … Read more

Today, we are excited to announce that Amazon Q Business—a fully managed generative-AI powered assistant that you can configure to answer questions, provide summaries and generate content based on your enterprise data—is now generally available in the Europe (Ireland) AWS Region. Since its launch, Amazon Q Business has been helping customers find information, gain insight, … Read more

This post is cowritten with Abdullahi Olaoye, Akshit Arora and Eliuth Triana Isaza at NVIDIA. As enterprises continue to push the boundaries of generative AI, scalable and efficient model training frameworks are essential. The NVIDIA NeMo Framework provides a robust, end-to-end solution for developing, customizing, and deploying large-scale AI models, while Amazon SageMaker HyperPod delivers … Read more

Today, we are excited to announce that the NeMo Retriever Llama3.2 Text Embedding and Reranking NVIDIA NIM microservices are available in Amazon SageMaker JumpStart. With this launch, you can now deploy NVIDIA’s optimized reranking and embedding models to build, experiment, and responsibly scale your generative AI ideas on AWS. In this post, we demonstrate how … Read more

As generative AI adoption accelerates across enterprises, maintaining safe, responsible, and compliant AI interactions has never been more critical. Amazon Bedrock Guardrails provides configurable safeguards that help organizations build generative AI applications with industry-leading safety protections. With Amazon Bedrock Guardrails, you can implement safeguards in your generative AI applications that are customized to your use … Read more

SQL is one of the key languages widely used across businesses, and it requires an understanding of databases and table metadata. This can be overwhelming for nontechnical users who lack proficiency in SQL. Today, generative AI can help bridge this knowledge gap for nontechnical users to generate SQL queries by using a text-to-SQL application. This … Read more

The rise of generative AI has significantly increased the complexity of building, training, and deploying machine learning (ML) models. It now demands deep expertise, access to vast datasets, and the management of extensive compute clusters. Customers also face the challenges of writing specialized code for distributed training, continuously optimizing models, addressing hardware issues, and keeping … Read more

In the rapidly evolving healthcare landscape, patients often find themselves navigating a maze of complex medical information, seeking answers to their questions and concerns. However, accessing accurate and comprehensible information can be a daunting task, leading to confusion and frustration. This is where the integration of cutting-edge technologies, such as audio-to-text translation and large language … Read more

Large language models (LLMs) have revolutionized the field of natural language processing, enabling machines to understand and generate human-like text with remarkable accuracy. However, despite their impressive language capabilities, LLMs are inherently limited by the data they were trained on. Their knowledge is static and confined to the information they were trained on, which becomes … Read more

Computer use is a breakthrough capability from Anthropic that allows foundation models (FMs) to visually perceive and interpret digital interfaces. This capability enables Anthropic’s Claude models to identify what’s on a screen, understand the context of UI elements, and recognize actions that should be performed such as clicking buttons, typing text, scrolling, and navigating between … Read more

Organizations building and deploying AI applications, particularly those using large language models (LLMs) with Retrieval Augmented Generation (RAG) systems, face a significant challenge: how to evaluate AI outputs effectively throughout the application lifecycle. As these AI technologies become more sophisticated and widely adopted, maintaining consistent quality and performance becomes increasingly complex. Traditional AI evaluation approaches … Read more

This post was co-written with Vishal Singh, Data Engineering Leader at Data & Analytics team of GoDaddy Generative AI solutions have the potential to transform businesses by boosting productivity and improving customer experiences, and using large language models (LLMs) in these solutions has become increasingly popular. However, inference of LLMs as single model invocations or … Read more

Open foundation models (FMs) allow organizations to build customized AI applications by fine-tuning for their specific domains or tasks, while retaining control over costs and deployments. However, deployment can be a significant portion of the effort, often requiring 30% of project time because engineers must carefully optimize instance types and configure serving parameters through careful … Read more

The integration of generative AI agents into business processes is poised to accelerate as organizations recognize the untapped potential of these technologies. Advancements in multimodal artificial intelligence (AI), where agents can understand and generate not just text but also images, audio, and video, will further broaden their applications. This post will discuss agentic AI driven … Read more

The Qwen 2.5 multilingual large language models (LLMs) are a collection of pre-trained and instruction tuned generative models in 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B (text in/text out and code out). The Qwen 2.5 fine tuned text-only models are optimized for multilingual dialogue use cases and outperform both previous generations of Qwen models, and … Read more

This post is cowritten with Harrison Hunter is the CTO and co-founder of MaestroQA. MaestroQA augments call center operations by empowering the quality assurance (QA) process and customer feedback analysis to increase customer satisfaction and drive operational efficiencies. They assist with operations such as QA reporting, coaching, workflow automations, and root cause analysis. In this … Read more

DeepSeek-R1, developed by AI startup DeepSeek AI, is an advanced large language model (LLM) distinguished by its innovative, multi-stage training process. Instead of relying solely on traditional pre-training and fine-tuning, DeepSeek-R1 integrates reinforcement learning to achieve more refined outputs. The model employs a chain-of-thought (CoT) approach that systematically breaks down complex queries into clear, logical … Read more