In this post, we explore how to modify your Regional access controls to specifically allow Amazon Bedrock cross-Region inference while maintaining broader Regional restrictions for other AWS services. We provide practical examples for both SCP modifications and AWS Control Tower implementations.

Today, we are announcing an enhanced private hub feature with several new capabilities that give organizations greater control over their ML assets. These enhancements include the ability to fine-tune SageMaker JumpStart models directly within the private hub, support for adding and managing custom-trained models, deep linking capabilities for associated notebooks, and improved model version management.

Generative AI has emerged as a game changer, offering unprecedented opportunities for game designers to push boundaries and create immersive virtual worlds. At the forefront of this revolution is Stability AI’s cutting-edge text-to-image AI model, Stable Diffusion 3.5 Large (SD3.5 Large), which is transforming the way we approach game environment creation. In this post, we explore how you can use SD3.5 Large to address practical gaming needs such as early concept art and character design.

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

In this post, we discuss the challenges faced by organizations when updating models in production. Then we deep dive into the new rolling update feature for inference components and provide practical examples using DeepSeek distilled models to demonstrate this feature. Finally, we explore how to set up rolling updates in different scenarios.

In this post, we discuss how to evaluate the performance of your knowledge base, including the metrics and data to use for evaluation. We also address some of the tactics and configuration changes that can improve specific metrics.

In this post, we demonstrate how to enhance enterprise productivity for your large language model (LLM) solution by using the Amazon Q index for ISVs.

Discover how to build a GenAI powered virtual IT troubleshooting assistant using Amazon Q Business. This innovative solution integrates with popular ITSM tools like ServiceNow, Atlassian Jira, and Confluence to streamline information retrieval and enhance collaboration across your organization. By harnessing the power of generative AI, this assistant can significantly boost operational efficiency and provide 24/7 support tailored to individual needs. Learn how to set up, configure, and leverage this solution to transform your enterprise information management.

Natural language processing models including the wide variety of contemporary large language models (LLMs) have become popular and useful in recent years as their application to a wide variety of problem domains have become increasingly capable, especially those related to text generation.

In this post, we explore how you can use these multi-modal generative AI models to streamline the management of technical documents. By extracting and structuring the key information from the source materials, the models can create a searchable knowledge base that allows you to quickly locate the data, formulas, and visualizations you need to support your work.

This post presents a comprehensive AIOps solution that combines various AWS services such as Amazon Bedrock, AWS Lambda, and Amazon CloudWatch to create an AI assistant for effective incident management. This solution also uses Amazon Bedrock Knowledge Bases and Amazon Bedrock Agents. The solution uses the power of Amazon Bedrock to enable the deployment of intelligent agents capable of monitoring IT systems, analyzing logs and metrics, and invoking automated remediation processes.

AWS provides a powerful tool called AWS Support Automation Workflows, which is a collection of curated AWS Systems Manager self-service automation runbooks. These runbooks are created by AWS Support Engineering with best practices learned from solving customer issues. They enable AWS customers to troubleshoot, diagnose, and remediate common issues with their AWS resources. In this post, we explore how to use the power of Amazon Bedrock Agents and AWS Support Automation Workflows to create an intelligent agent capable of troubleshooting issues with AWS resources.

In this post, we demonstrate how to use Amazon Bedrock in SageMaker Unified Studio to build a generative AI application to integrate with an existing endpoint and database.

In this post, we explore why Asure used the Amazon Web Services (AWS) post-call analytics (PCA) pipeline that generated insights across call centers at scale with the advanced capabilities of generative AI-powered services such as Amazon Bedrock and Amazon Q in QuickSight. Asure chose this approach because it provided in-depth consumer analytics, categorized call transcripts around common themes, and empowered contact center leaders to use natural language to answer queries. This ultimately allowed Asure to provide its customers with improvements in product and customer experiences.

Today, we're excited to announce the general availability of Amazon Bedrock Data Automation, a powerful, fully managed capability within Amazon Bedrock that seamlessly transforms unstructured multimodal data into structured, application-ready insights with high accuracy, cost efficiency, and scalability.

This post is in three parts; they are: • Building a simple Q&A system • Handling Large Contexts • Building an Expert System Question and answering system is not just to throw a question at a model and get an answer.

Be sure to check out the previous articles in this series: •

In this blog post, we showcase a powerful solution that seamlessly integrates AWS generative AI capabilities in the form of large language models (LLMs) based on Amazon Bedrock into the Office experience. By harnessing the latest advancements in generative AI, we empower employees to unlock new levels of efficiency and creativity within the tools they already use every day.

In this post, I will share some of these customers’ remarkable journeys, offering practical insights for any organization looking to harness the power of generative AI.

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.

For many people studying data science,

In this blog post, we explore how to integrate NeMo 2.0 with SageMaker HyperPod to enable efficient training of large language models (LLMs). We cover the setup process and provide a step-by-step guide to running a NeMo job on a SageMaker HyperPod cluster.

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 to get started with these models on SageMaker JumpStart.

Today, we’re announcing a significant enhancement to Amazon Bedrock Guardrails: AWS Identity and Access Management (IAM) policy-based enforcement. This powerful capability enables security and compliance teams to establish mandatory guardrails for every model inference call, making sure organizational safety policies are consistently enforced across AI interactions. This feature enhances AI governance by enabling centralized control over guardrail implementation.

Debugging machine learning models entails inspecting, discovering, and fixing possible errors in the internal mechanisms of these models.

The transformers library is a Python library that provides a unified interface for working with different transformer models.

Large language models (LLMs) are a big step forward in artificial intelligence.

The large language model (LLM) has become a cornerstone of many AI applications.

Be sure to check out the previous articles in this series: •

Time series forecasting is a statistical technique used to analyze historical data points and predict future values based on temporal patterns.