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Unlock model insights with log probability support for Amazon Bedrock Custom Model Import | Artificial Intelligence
In this post, we explore how log probabilities work with imported models in Amazon Bedrock. You will learn what log probabilities are, how to enable them in your API calls, and how to interpret the returned data. We also highlight practical applications—from detecting potential hallucinations to optimizing RAG systems and evaluating fine-tuned models—that demonstrate how these insights can improve your AI applications, helping you build more trustworthy solutions with your custom models.
Automate advanced agentic RAG pipeline with Amazon SageMaker AI | Artificial Intelligence
In this post, we walk through how to streamline your RAG development lifecycle from experimentation to automation, helping you operationalize your RAG solution for production deployments with Amazon SageMaker AI, helping your team experiment efficiently, collaborate effectively, and drive continuous improvement.
Migrate from Anthropic’s Claude 3.5 Sonnet to Claude 4 Sonnet on Amazon Bedrock | Artificial Intelligence
This post provides a systematic approach to migrating from Anthropic’s Claude 3.5 Sonnet to Claude 4 Sonnet on Amazon Bedrock. We examine the key model differences, highlight essential migration considerations, and deliver proven best practices to transform this necessary transition into a strategic advantage that drives measurable value for your organization.
Accelerate your data and AI workflows by connecting to Amazon SageMaker Unified Studio from Visual Studio Code | AWS Big Data Blog
In this post, we demonstrate how to connect your local VS Code to SageMaker Unified Studio so you can build complete end-to-end data and AI workflows while working in your preferred development environment.
Create a private workforce on Amazon SageMaker Ground Truth with the AWS CDK | Artificial Intelligence
In this post, we present a complete solution for programmatically creating private workforces on Amazon SageMaker AI using the AWS Cloud Development Kit (AWS CDK), including the setup of a dedicated, fully configured Amazon Cognito user pool.
Enhance video understanding with Amazon Bedrock Data Automation and open-set object detection | Artificial Intelligence
In real-world video and image analysis, businesses often face the challenge of detecting objects that weren’t part of a model’s original training set. This becomes especially difficult in dynamic environments where new, unknown, or user-defined objects frequently appear. In this post, we explore how Amazon Bedrock Data Automation uses OSOD to enhance video understanding.
Use the Amazon DataZone upgrade domain to Amazon SageMaker and expand to new SQL analytics, data processing, and AI uses cases | AWS Big Data Blog
Don’t miss our upcoming webinar! Register here to join AWS experts as they dive deeper and share practical insights for upgrading to SageMaker. Amazon DataZone and Amazon SageMaker announced a new feature that allows an Amazon DataZone domain to be upgraded to the next generation of SageMaker, making the investment customers put into developing Amazon […]
Migrating from API keys to service account tokens in Grafana dashboards using Terraform | AWS Big Data Blog
In this blog post, we walk through how to migrate from API keys to service account tokens when automating Amazon Managed Grafana resource management. We will also show how to securely store tokens using AWS Secrets Manager and automate token rotation with AWS Lambda.
How Skello uses Amazon Bedrock to query data in a multi-tenant environment while keeping logical boundaries | Artificial Intelligence
Skello is a leading human resources (HR) software as a service (SaaS) solution focusing on employee scheduling and workforce management. Catering to diverse sectors such as hospitality, retail, healthcare, construction, and industry, Skello offers features including schedule creation, time tracking, and payroll preparation. We dive deep into the challenges of implementing large language models (LLMs) for data querying, particularly in the context of a French company operating under the General Data Protection Regulation (GDPR).
Implement network connectivity patterns for Oracle Database@AWS | AWS Database Blog
Oracle Database@AWS (ODB@AWS) is an offering you can use to access Oracle Exadata infrastructure managed by Oracle Cloud Infrastructure (OCI) within Amazon Web Services (AWS) data centers. You can use ODB@AWS to migrate your Oracle Exadata workloads to AWS while maintaining the same performance and features as your on-premises Oracle Exadata deployments. You benefit from […]
Oldcastle accelerates document processing with Amazon Bedrock | Artificial Intelligence
This post explores how Oldcastle partnered with AWS to transform their document processing workflow using Amazon Bedrock with Amazon Textract. We discuss how Oldcastle overcame the limitations of their previous OCR solution to automate the processing of hundreds of thousands of POD documents each month, dramatically improving accuracy while reducing manual effort.
TII Falcon-H1 models now available on Amazon Bedrock Marketplace and Amazon SageMaker JumpStart | Artificial Intelligence
We are excited to announce the availability of the Technology Innovation Institute (TII)’s Falcon-H1 models on Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, developers and data scientists can now use six instruction-tuned Falcon-H1 models (0.5B, 1.5B, 1.5B-Deep, 3B, 7B, and 34B) on AWS, and have access to a comprehensive suite of hybrid architecture models that combine traditional attention mechanisms with State Space Models (SSMs) to deliver exceptional performance with unprecedented efficiency.
Build trustworthy AI agents with Amazon Bedrock AgentCore Observability | Artificial Intelligence
In this post, we walk you through implementation options for both agents hosted on Amazon Bedrock AgentCore Runtime and agents hosted on other services like Amazon Elastic Compute Cloud (Amazon EC2), Amazon Elastic Kubernetes Service (Amazon EKS), AWS Lambda, or alternative cloud providers. We also share best practices for incorporating observability throughout the development lifecycle.
Build a streaming data mesh using Amazon Kinesis Data Streams | AWS Big Data Blog
AWS provides two primary solutions for streaming ingestion and storage: Amazon Managed Streaming for Apache Kafka (Amazon MSK) or Amazon Kinesis Data Streams. These services are key to building a streaming mesh on AWS. In this post, we explore how to build a streaming mesh using Kinesis Data Streams.
How London Stock Exchange Group is detecting market abuse with their AI-powered Surveillance Guide on Amazon Bedrock | Artificial Intelligence
In this post, we explore how London Stock Exchange Group (LSEG) used Amazon Bedrock and Anthropic's Claude foundation models to build an automated system that significantly improves the efficiency and accuracy of market surveillance operations.
Decrease your storage costs with Amazon OpenSearch Service index rollups | AWS Big Data Blog
Amazon OpenSearch Service is a fully managed service to support search, log analytics, and generative AI Retrieval Augment Generation (RAG) workloads in the AWS Cloud. It simplifies the deployment, security, and scaling of OpenSearch clusters. As organizations scale their log analytics workloads by continuously collecting and analyzing vast amounts of data, they often struggle to […]
Powering innovation at scale: How AWS is tackling AI infrastructure challenges | Artificial Intelligence
As generative AI continues to transform how enterprises operate—and develop net new innovations—the infrastructure demands for training and deploying AI models have grown exponentially. Traditional infrastructure approaches are struggling to keep pace with today’s computational requirements, network demands, and resilience needs of modern AI workloads. At AWS, we’re also seeing a transformation across the technology […]
Accelerate your model training with managed tiered checkpointing on Amazon SageMaker HyperPod | Artificial Intelligence
AWS announced managed tiered checkpointing in Amazon SageMaker HyperPod, a purpose-built infrastructure to scale and accelerate generative AI model development across thousands of AI accelerators. Managed tiered checkpointing uses CPU memory for high-performance checkpoint storage with automatic data replication across adjacent compute nodes for enhanced reliability. In this post, we dive deep into those concepts and understand how to use the managed tiered checkpointing feature.
Data consistency with AWS DMS data resync | AWS Database Blog
In this post, we deep dive into AWS Database Migration Service Data Resync, a feature that was introduced in DMS version 3.6.1 to detect and resolve data inconsistencies during database migrations, eliminating the need for manual intervention.
Announcing SageMaker Unified Studio Workshops for Financial Services | AWS Big Data Blog
In this post, we’re excited to announce the release of four Amazon SageMaker Unified Studio publicly available workshops that are specific to each FSI segment: insurance, banking, capital markets, and payments. These workshops can help you learn how to deploy Amazon SageMaker Unified Studio effectively for business use cases.
Build and scale adoption of AI agents for education with Strands Agents, Amazon Bedrock AgentCore, and LibreChat | Artificial Intelligence
This post demonstrates how to quickly build sophisticated AI agents using Strands Agents, scale them reliably with Amazon Bedrock AgentCore, and make them accessible through LibreChat’s familiar interface to drive immediate user adoption across your institution.
The power of AI in driving personalized product discovery at Snoonu | Artificial Intelligence
In this post, we share how Snoonu, a leading ecommerce platform in the Middle East, transformed their product discovery experience using AI-powered personalization. In this post, we share how Snoonu, a leading ecommerce platform in the Middle East, transformed their product discovery experience using AI-powered personalization.
Introducing enhanced AI assistance in Amazon SageMaker Unified Studio: Agentic chat, Amazon Q Developer CLI, and MCP integration | AWS Big Data Blog
In this post, we will walk through how you can use the improved Amazon Q Developer chat and the new built-in Amazon Q Developer CLI in SageMaker Unified Studio for coding ETL tasks, to fix code errors, and generate ML development workflows. Both interfaces use MCP to read files, run commands, and interact with AWS services directly from the IDE. You can also configure additional MCP servers to extend Amazon Q Developer’s capabilities with custom tools and integrations specific to your workflow.
Skai uses Amazon Bedrock Agents to significantly improve customer insights by revolutionized data access and analysis | Artificial Intelligence
Skai (formerly Kenshoo) is an AI-driven omnichannel advertising and analytics platform designed for brands and agencies to plan, launch, optimize, and measure paid media across search, social, retail media marketplaces and other “walled-garden” channels from a single interface. In this post, we share how Skai used Amazon Bedrock Agents to improve data access and analysis and improve customer insights.
Accelerate AWS Glue Zero-ETL data ingestion using Salesforce Bulk API | AWS Big Data Blog
AWS Glue Zero ETL (extract, transform, and load) now supports Salesforce Bulk API, delivering substantial performance gains compared to Salesforce REST API for large-scale data integration for targets such as Amazon SageMaker lakehouse and Amazon Redshift. In this blog post, we show you how to use Zero-ETL powered by AWS Glue with Salesforce Bulk API to accelerate your data integration processes.
Introducing restricted classification terms for governed classification in Amazon SageMaker Catalog | AWS Big Data Blog
Security and compliance concerns are key considerations when customers across industries rely on Amazon SageMaker Catalog. Customers use SageMaker Catalog to organize, discover, and govern data and machine learning (ML) assets. A common request from domain administrators is the ability to enforce governance controls on certain metadata terms that carry compliance or policy significance. Examples […]
Maximize HyperPod Cluster utilization with HyperPod task governance fine-grained quota allocation | Artificial Intelligence
We are excited to announce the general availability of fine-grained compute and memory quota allocation with HyperPod task governance. With this capability, customers can optimize Amazon SageMaker HyperPod cluster utilization on Amazon Elastic Kubernetes Service (Amazon EKS), distribute fair usage, and support efficient resource allocation across different teams or projects. For more information, see HyperPod task governance best […]
Group database tables under AWS Database Migration Service tasks for PostgreSQL source engine | AWS Database Blog
AWS DMS accommodates a broad range of source and target data repositories, such as relational databases, data warehouses, and NoSQL databases. Proper preparation and design are vital for a successful migration process, especially when it comes to optimizing performance and addressing potential delay issues. In this blog post, we offer guidance about recognizing potential root causes of complete load and CDC delays early in the process and provide suggestions for optimally clustering tables to achieve the best performance for an AWS DMS task.
Exploring the Real-Time Race Track with Amazon Nova | Artificial Intelligence
This post explores the Real-Time Race Track (RTRT), an interactive experience built using Amazon Nova in Amazon Bedrock, that lets fans design, customize, and share their own racing circuits. We highlight how generative AI capabilities come together to deliver strategic racing insights such as pit timing and tire choices, and interactive features like an AI voice assistant and a retro-style racing poster.
Accelerating HPC and AI research in universities with Amazon SageMaker HyperPod | Artificial Intelligence
In this post, we demonstrate how a research university implemented SageMaker HyperPod to accelerate AI research by using dynamic SLURM partitions, fine-grained GPU resource management, budget-aware compute cost tracking, and multi-login node load balancing—all integrated seamlessly into the SageMaker HyperPod environment.
Automating vector embedding generation in Amazon Aurora PostgreSQL with Amazon Bedrock | AWS Database Blog
In this post, we explore several approaches for automating the generation of vector embedding in Amazon Aurora PostgreSQL-Compatible Edition when data is inserted or modified in the database. Each approach offers different trade-offs in terms of complexity, latency, reliability, and scalability, allowing you to choose the best fit for your specific application needs.
Achieve full control over your data encryption using customer managed keys in Amazon Managed Service for Apache Flink | AWS Big Data Blog
Encryption of both data at rest and in transit is a non-negotiable feature for most organizations. Furthermore, organizations operating in highly regulated and security-sensitive environments—such as those in the financial sector—often require full control over the cryptographic keys used for their workloads. Amazon Managed Service for Apache Flink makes it straightforward to process real-time data […]
Build character consistent storyboards using Amazon Nova in Amazon Bedrock – Part 2 | Artificial Intelligence
In this post, we take an animated short film, Picchu, produced by FuzzyPixel from Amazon Web Services (AWS), prepare training data by extracting key character frames, and fine-tune a character-consistent model for the main character Mayu and her mother, so we can quickly generate storyboard concepts for new sequels like the following images.
Reduce your Amazon ElastiCache costs by up to 60% with Valkey and CUDOS | AWS Database Blog
In this post, we show you how to save costs on Amazon ElastiCache by upgrading your cluster engine to ElastiCache for Valkey. If you’re currently using ElastiCache for Redis OSS, you can achieve up to 60% cost savings by upgrading to Valkey.
Build character consistent storyboards using Amazon Nova in Amazon Bedrock – Part 1 | Artificial Intelligence
The art of storyboarding stands as the cornerstone of modern content creation, weaving its essential role through filmmaking, animation, advertising, and UX design. Though traditionally, creators have relied on hand-drawn sequential illustrations to map their narratives, today’s AI foundation models (FMs) are transforming this landscape. FMs like Amazon Nova Canvas and Amazon Nova Reel offer […]
Deep dive into the Amazon Managed Service for Apache Flink application lifecycle – Part 2 | AWS Big Data Blog
In Part 1 of this series, we discussed fundamental operations to control the lifecycle of your Amazon Managed Service for Apache Flink application. In this post, we explore failure scenarios that can happen during normal operations or when you deploy a change or scale the application, and how to monitor operations to detect and recover when something goes wrong.
Deep dive into the Amazon Managed Service for Apache Flink application lifecycle – Part 1 | AWS Big Data Blog
In this two-part series, we explore what happens during an application’s lifecycle. This post covers core concepts and the application workflow during normal operations. In Part 2, we look at potential failures, how to detect them through monitoring, and ways to quickly resolve issues when they occur.