Amazon Web Services (AWS)

Today, Amazon Simple Email Services (SES) announces the availability of Global Endpoints, a feature for resilient sending through two commercial AWS Regions. Global Endpoints works with SES APIv2 and allows customers to choose a primary and secondary Region which accommodate email sending workloads in an equal split under normal circumstances. If either region suffers an impairment, traffic shifts away from the affected Region towards the other, ensuring that email sending continues. Unlike manual multi-region setups, Global Endpoints simplifies the synchronization of verified identities, approved sending limits, and configuration sets between the two chosen Regions. B…

Starting today, AWS Network Firewall is available in the AWS Asia Pacific (Malaysia) Region, enabling customers to deploy essential network protections for all their Amazon Virtual Private Clouds (VPCs). AWS Network Firewall is a managed firewall service that is easy to deploy. The service automatically scales with network traffic volume to provide high-availability protections without the need to set up and maintain the underlying infrastructure. It is integrated with AWS Firewall Manager to provide you with central visibility and control over your firewall policies across multiple AWS accounts. To see which regions AWS Network Firewall is available in, visit the A…

Amazon Keyspaces (for Apache Cassandra) is a scalable, serverless, highly available, and fully managed Apache Cassandra-compatible database service that offers 99.999% availability. Today, Amazon Keyspaces added support for Cassandra’s User Defined Types (UDTs) in the AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. With support for UDTs, you can continue using any custom data types that are defined in your Cassandra workloads in Keyspaces without making schema modifications. With this launch, you can use UDTs in the primary key of your tables, allowing you to index your data on more complex and richer data types. Additionally, UDTs enable you to create data …

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8g instances are available in AWS Asia Pacific (Tokyo) region. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 R8g instances are ideal for memory-intensive workloads such as databases, in-memory caches, and real-time big data analytics. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads. AWS Graviton4-based Amazon EC2 instances deliver the best performa…

Amazon Keyspaces (for Apache Cassandra) is a scalable, serverless, highly available, and fully managed Apache Cassandra-compatible database service that offers 99.999% availability. Today, Amazon Keyspaces added support for frozen collections in the AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. With support for frozen collections, the primary keys in your tables can contain collections, allowing you to index your tables on more complex and richer data types. Additionally, using frozen collections, you can create nested collections. Nested collections enable you to model your data in a more real-world way and efficient manner. The AWS console extends the na…

We are excited to announce the general availability of new multilingual streaming speech recognition models (ASR-2.0) in Amazon Lex. These models enhance recognition accuracy through two specialized groupings: one European-based model supporting Portuguese, Catalan, French, Italian, German, and Spanish, and another Asia Pacific-based model supporting Chinese, Korean, and Japanese. These Amazon Lex multilingual streaming models leverage shared language patterns within each group to deliver improved recognition accuracy. The models particularly excel at recognizing alphanumeric speech, making it easier to accurately understand customer utterances that are often needed to…

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) M8g instances are available in AWS Europe (Spain) region. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 M8g instances are built for general-purpose workloads, such as application servers, microservices, gaming servers, midsize data stores, and caching fleets. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads. AWS Graviton4-based Amazon EC2 instances d…

Organizations are building data-driven applications to guide business decisions, improve agility, and drive innovation. Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Data engineers use data warehouses, data lakes, and analytics tools to load, transform, clean, and aggregate data. Data scientists use notebook environments (such as JupyterLab) to create predictive models for different target segments. However, building advanced data-driven applications poses several challenges. First, it can be time consuming for users to learn multiple services’ development experiences. Se…

This blog was authored by Johannes Brück, Senior Staff Engineer (Personio), Donald Dragoti, Lead Platform Engineer (Personio), Steve Flinchbaugh, Lead Platform Engineer (Personio), Maximilian Schellhorn, Senior Solutions Architect (AWS) and Dionysios Kakaletris, Technical Account Manager (AWS). Migrating your Amazon Elastic Kubernetes Service (Amazon EKS) nodes to use AWS Graviton based Amazon Elastic Compute Cloud (Amazon EC2) instances can lead up to 40% better price performance and use up to 60% less energy than comparable EC2 instances for the same performance. However, the application rollout on multiple CPU architectures necessitates preparation and adaption to y…

Amazon MQ now supports AWS PrivateLink (interface VPC endpoint) to connect directly to the Amazon MQ API in your virtual private cloud (VPC) instead of connecting over the internet. When you use AWS PrivateLink, communication between your VPC and Amazon MQ API is conducted entirely within the AWS network, providing an optimized secure pathway for your data. An AWS PrivateLink endpoint connects your VPC directly to the Amazon MQ API. The instances in your VPC don't need public IP addresses to communicate with the Amazon MQ API. To use Amazon MQ through your VPC, you can connect from an instance that is inside your VPC, or connect your private network to your VPC by usin…

Amazon Simple Email Services (SES) announces the availability of Deterministic Easy DKIM (DEED), a new form of global identity which simplifies the use of DomainKeys Identified Mail (DKIM) management for SES first-party sender customers and independent solution vendors (ISVs). DEED expands the existing Easy DKIM solution from SES and enables it to work across all commercial AWS Regions, instead of being limited to just one. While Easy DKIM required domain name system (DNS) lookups to be made in the Region where the identity was verified, DEED expands that capability so customers can use the same identity across multiple Regions without making any DNS setup changes. Custom…

Today, AWS announces three new updates to AWS IoT Core for LoRaWAN: IPv6 support, enhanced Firmware Update Over-The-Air (FUOTA) with advanced logging capabilities, and console-based gateway firmware updates, improving fleet management, scalability, reliability, and user experience for Internet of Things (IoT) applications. AWS IoT Core for LoRaWAN is a fully-managed cloud service that makes it easy to connect, manage, and monitor wireless devices that use low-power, long-range wide area network (LoRaWAN) technology. With the new feature updates, developers can now assign IPv6 address to their LoRaWAN-based devices and gateways and coexist with other IPv4 devices in the…

Amazon Bedrock Guardrails enable you to implement safeguards for your generative AI applications based on your use cases and responsible AI policies. Starting today, we are excited to announce that Amazon Bedrock Guardrails are even more cost-effective with reduced pricing by up to 85%. Amazon Bedrock Guardrails help you build safe, generative AI applications by filtering undesirable content, redacting personally identifiable information (PII), and enhancing content safety and privacy. You can configure policies for content filters, denied topics, word filters, PII redaction, contextual grounding checks, and Automated Reasoning checks (preview), to tailor safeguards to…

As data is generated at an unprecedented rate, streaming solutions have become essential for businesses seeking to harness near real-time insights. Streaming data—from social media feeds, IoT devices, e-commerce transactions, and more—requires robust platforms that can process and analyze data as it arrives, enabling immediate decision-making and actions. This is where Apache Spark Structured Streaming comes into play. It offers a high-level API that simplifies the complexities of streaming data, allowing developers to write streaming jobs as if they were batch jobs, but with the power to process data in near real time. Spark Structured Streaming integrates seamlessly w…

AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) C6in and M6in instances in Dallas Local Zone. These instances are powered by 3rd Generation Intel Xeon Scalable processors with an all-core turbo frequency of up to 3.5 GHz. They are x86-based general purpose and compute-optimized instances offering up to 200 Gbps of network bandwidth. The instances are built on AWS Nitro System, which is a dedicated and lightweight hypervisor that delivers the compute and memory resources of the host hardware to your instances for better overall performance and security. You can take advantage of the higher network bandwidth to scale the performance for a…

Today, AWS Resource Groups is adding support for an additional 405 resource types for tag-based Resource Groups. Customers can now use Resource Groups to group and manage resources from services such as Bedrock, Chime, and Quicksight. AWS Resource Groups enables you to model, manage and automate tasks on large numbers of AWS resources by using tags to logically group your resources. You can create logical collections of resources such as applications, projects, and cost centers, and manage them on dimensions such as cost, performance, and compliance in AWS services such as myApplications, AWS Systems Manager and Amazon CloudWatch. Resource Groups expanded resource t…

Amazon RDS for SQL Server now offers enhanced control over backup and restore operations with new custom parameters. This update allows database administrators to fine-tune their processes, potentially improving efficiency and reducing operation times. The new parameters are available for the rds_backup_database, rds_restore_database, and rds_restore_log stored procedures. You can now specify the BLOCKSIZE, MAXTRANSFERSIZE, and BUFFERCOUNT parameters for backup and restore operations. These granular controls can help optimize performance based on your specific database characteristics and workload patterns. These customizable parameters are particularly useful when cus…

Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. With its massively parallel processing (MPP) architecture and columnar data storage, Amazon Redshift delivers high price-performance for complex analytical queries against large datasets. To interact with and analyze data stored in Amazon Redshift, AWS provides the Amazon Redshift Query Editor V2, a web-based tool that allows you to explore, analyze, and share data using SQL. The Query Editor V2 offers a user-friendly interface for connecting to your Redshift clusters, executing queries, and visualizing results. As organizatio…

Starting today, you can record thumbnail images in Amazon Interactive Video Service (Amazon IVS) Real-Time Streaming. When thumbnail recording is enabled, Amazon IVS automatically generates images at the interval you configure and stores them in the Amazon S3 bucket you select. Thumbnails can be used for preview images in content discovery or as part of content moderation workflows. There is no additional cost for enabling thumbnail recording, but standard Amazon S3 storage and request costs apply. Amazon IVS is a managed live streaming solution that is designed to make low-latency or real-time video available to viewers around the world. Video ingest and delivery are a…

Welcome to December’s post announcing new training course launches and certification updates — helping equip you and your teams with the skills to work with AWS services and solutions. Missed our last course update? Check it out here. This month, we launched nine new digital training products on AWS Skill Builder including five new AWS Builder Labs, a new AWS Jam focused on troubleshooting AWS Web Development issues in a gamified learning environment, and one new AWS Digital Classroom course. We also launched AWS Learning Assistant for AWS Builder Labs, a new AI-powered, chat-based guide that enhances self-paced learning by providing real-time responses and insights t…

Given the importance of data in the world today, organizations face the dual challenges of managing large-scale, continuously incoming data while vetting its quality and reliability. The importance of publishing only high-quality data can’t be overstated—it’s the foundation for accurate analytics, reliable machine learning (ML) models, and sound decision-making. Equally crucial is the ability to segregate and audit problematic data, not just for maintaining data integrity, but also for regulatory compliance, error analysis, and potential data recovery. AWS Glue is a serverless data integration service that you can use to effectively monitor and manage data quality throu…

In today’s data-driven world, tracking and analyzing changes over time has become essential. As organizations process vast amounts of data, maintaining an accurate historical record is crucial. History management in data systems is fundamental for compliance, business intelligence, data quality, and time-based analysis. It enables organizations to maintain audit trails, perform trend analysis, identify data quality issues, and conduct point-in-time reporting. When combined with Change Data Capture (CDC), which identifies and captures database changes, history management becomes even more potent. Common use cases for historical record management in CDC scenarios span var…

Model Evaluation on Amazon Bedrock allows you to evaluate, compare, and select the best foundation models for your use case. Amazon Bedrock offers a choice of using an LLM-as-a-judge, programmatic evaluation, and human evaluation. You can use an LLM-as-a-judge for metrics such as correctness, completeness, and coherence, as well as responsible AI metrics such as answer refusal and harmfulness. Programmatic evaluation offers algorithms for metrics such as accuracy, robustness, and toxicity. Additionally, for those metrics or subjective and custom metrics, such as friendliness or style, you can set up a human evaluation workflow with a few clicks. Human evaluation leverages …

AWS is announcing the general availability of two new Amazon EC2 High Memory U7i instances with 6TiB and 8TiB of memory. U7i-6tb and U7i-8tb are powered by 4th Generation Intel Xeon Scalable processors and offer 448 vCPUs, delivering up to 35% better performance and up to 15% better price performance versus comparable AWS EC2 High Memory U-1 instances. These instances extend the U7i instance family, providing customers greater flexibility to select the right instance for the right workload. U7i instances are ideal to run large in-memory databases such as SAP HANA, Oracle, and SQL Server. U7i instances are built on the AWS Nitro system, a collection of AWS designed hard…

Amazon EC2 U7in-24tb instances are now available in AWS GovCloud (US-West) Region. U7in-24tb instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids) delivering up to 135% more compute performance over existing U-1 instances. U7in-24tb instances offer 24TiB of DDR5 memory enabling customers to scale transaction processing throughput in a fast-growing data environment. U7in-24tb instance supports 896 vCPUs, the most vCPUs in the AWS cloud and support up to 100Gbps Elastic Block Storage (EBS), enabling customers to load data faster into memory and improve their backup speed. U7in-24tb instances …

This past week at AWS re:Invent, we celebrated the organizations that went above and beyond to certify staff in AI/ML skills. Maureen Lonergan, VP, AWS Training and Certification and Diana Godwin, Director, AWS Certification hosted a reception at the AWS Certification Lounge to award AWS AI Skills Champion Trophies to these organizations as AWS AI Certification Early Adopters. These outstanding organizations were recognized for their commitment to building AI/ML skills amongst their employees or to enable their customers by helping them to earn AWS Certification on AWS Certified AI Practitioner and AWS Certified Machine Learning Engineer – Associate. The winners below r…

Starting today, Amazon EC2 Hpc7a instances are available in additional AWS Region Europe (Paris). EC2 Hpc7a instances are powered by 4th generation AMD EPYC processors with up to 192 cores, and 300 Gbps of Elastic Fabric Adapter (EFA) network bandwidth for fast and low-latency internode communications. Hpc7a instances feature Double Data Rate 5 (DDR5) memory, which enables high-speed access to data in memory. Hpc7a instances are ideal for compute-intensive, tightly coupled, latency-sensitive high performance computing (HPC) workloads, such as computational fluid dynamics (CFD), weather forecasting, and multiphysics simulations, helping you scale more efficiently on few…

Starting today, Amazon EC2 Hpc6id instances are available in additional AWS Region Europe (Paris). These instances are optimized to efficiently run memory bandwidth-bound, data-intensive high performance computing (HPC) workloads, such as finite element analysis and seismic reservoir simulations. With EC2 Hpc6id instances, you can lower the cost of your HPC workloads while taking advantage of the elasticity and scalability of AWS. EC2 Hpc6id instances are powered by 64 cores of 3rd Generation Intel Xeon Scalable processors with an all-core turbo frequency of 3.5 GHz, 1,024 GB of memory, and up to 15.2 TB of local NVMe solid state drive (SSD) storage. EC2 Hpc6id instanc…

Amazon Aurora PostgreSQL is now available as a quick create vector store in Amazon Bedrock Knowledge Bases. With the new Aurora quick create option, developers and data scientists building generative AI applications can select Aurora PostgreSQL as their vector store with one click to deploy an Aurora Serverless cluster preconfigured with pgvector in minutes. Aurora Serverless is an on-demand, autoscaling configuration where capacity is adjusted automatically based on application demand, making it ideal as a developer vector store. Knowledge Bases securely connects foundation models (FMs) running in Bedrock to your company data sources for Retrieval Augmented Generation…

Today, we are introducing the new ModelTrainer class and enhancing the ModelBuilder class in the SageMaker Python SDK. These updates streamline training workflows and simplify inference deployments. The ModelTrainer class enables customers to easily set up and customize distributed training strategies on Amazon SageMaker. This new feature accelerates model training times, optimizes resource utilization, and reduces costs through efficient parallel processing. Customers can smoothly transition their custom entry points and containers from a local environment to SageMaker, eliminating the need to manage infrastructure. ModelTrainer simplifies configuration by reducing pa…

Amazon RDS (Relational Database Service) Performance Insights expands the availability of its on-demand analysis experience to 15 new regions. This feature is available for Aurora MySQL, Aurora PostgreSQL, and RDS for PostgreSQL engines. This on-demand analysis experience, which was previously available in only 15 regions, is now available in all commercial regions. This feature allows you to analyze Performance Insights data for a time period of your choice. You can learn how the selected time period differs from normal, what went wrong, and get advice on corrective actions. Through simple-to-understand graphs and explanations, you can identify the chief contributors …

We are excited to announce two new capabilities in SageMaker Inference that significantly enhance the deployment and scaling of generative AI models: Container Caching and Fast Model Loader. These innovations address critical challenges in scaling large language models (LLMs) efficiently, enabling faster response times to traffic spikes and more cost-effective scaling. By reducing model loading times and accelerating autoscaling, these features allow customers to improve the responsiveness of their generative AI applications as demand fluctuates, particularly benefiting services with dynamic traffic patterns. Container Caching dramatically reduces the time required to …

AWS Config added support for a service-linked recorder, a new type of AWS Config recorder that is managed by an AWS service and can record configuration data on service-specific resources, such as the new Amazon CloudWatch telemetry configurations audit. By enabling the service-linked recorder in Amazon CloudWatch, you gain centralized visibility into critical AWS service telemetry configurations, such as Amazon VPC Flow Logs, Amazon EC2 Detailed Metrics, and AWS Lambda Traces. With service-linked recorders, an AWS service can deploy and manage an AWS Config recorder on your behalf to discover resources and utilize the configuration data to provide differentiated featu…

Amazon CloudWatch now offers centralized visibility into critical AWS service telemetry configurations, such as Amazon VPC Flow Logs, Amazon EC2 Detailed Metrics, and AWS Lambda Traces. This enhanced visibility enables central DevOps teams, system administrators, and service teams to identify potential gaps in their infrastructure monitoring setup. The telemetry configuration auditing experience seamlessly integrates with AWS Config to discover AWS resources, and can be turned on for the entire organization using the new AWS Organizations integration with Amazon CloudWatch. With visibility into telemetry configurations, you can identify monitoring gaps that might have …

This post was co-written with Dr. Jan Melchior at BASF Digital Farming GmbH and xarvio Digital Farming Solutions. BASF Digital Farming’s mission is to support farmers worldwide with cutting-edge digital agronomic decision advice by using its main crop optimization platform, xarvio FIELD MANAGER. This necessitates providing the most recent satellite imagery available as quickly as possible. This blog post describes the serverless architecture developed by BASF Digital Farming for efficiently downloading and supplying satellite imagery from various providers to support its xarvio platform. Figure 1. Screenshot showing the xarvio Field Manager platform Architectur…

This post was written by Eunice Aguilar and Francisco Rodera from REA Group. Enterprises that need to share and access large amounts of data across multiple domains and services need to build a cloud infrastructure that scales as need changes. REA Group, a digital business that specializes in real estate property, solved this problem using Amazon Managed Streaming for Apache Kafka (Amazon MSK) and a data streaming platform called Hydro. REA Group’s team of more than 3,000 people is guided by our purpose: to change the way the world experiences property. We help people with all aspects of their property experience—not just buying, selling, and renting—through the riche…

Organizations are increasingly using data to make decisions and drive innovation. However, building data-driven applications can be challenging. It often requires multiple teams working together and integrating various data sources, tools, and services. For example, creating a targeted marketing app involves data engineers, data scientists, and business analysts using different systems and tools. This complexity leads to several issues: it takes time to learn multiple systems, it’s difficult to manage data and code across different services, and controlling access for users across various systems is complicated. Currently, organizations often create custom solutions to co…

AWS Glue 5.0 supports fine-grained access control (FGAC) based on your policies defined in AWS Lake Formation. FGAC enables you to granularly control access to your data lake resources at the table, column, and row levels. This level of control is essential for organizations that need to comply with data governance and security regulations, or those that deal with sensitive data. Lake Formation makes it straightforward to build, secure, and manage data lakes. It allows you to define fine-grained access controls through grant and revoke statements, similar to those used with relational database management systems (RDBMS), and automatically enforce those policies using co…

Open table formats are emerging in the rapidly evolving domain of big data management, fundamentally altering the landscape of data storage and analysis. These formats, exemplified by Apache Iceberg, Apache Hudi, and Delta Lake, addresses persistent challenges in traditional data lake structures by offering an advanced combination of flexibility, performance, and governance capabilities. By providing a standardized framework for data representation, open table formats break down data silos, enhance data quality, and accelerate analytics at scale. As organizations grapple with exponential data growth and increasingly complex analytical requirements, these formats are tra…

AWS Glue is a serverless, scalable data integration service that makes it simple to discover, prepare, move, and integrate data from multiple sources. Today, we are launching AWS Glue 5.0, a new version of AWS Glue that accelerates data integration workloads in AWS. AWS Glue 5.0 upgrades the Spark engines to Apache Spark 3.5.2 and Python 3.11, giving you newer Spark and Python releases so you can develop, run, and scale your data integration workloads and get insights faster. This post describes what’s new in AWS Glue 5.0, performance improvements, key highlights on Spark and related libraries, and how to get started on AWS Glue 5.0. What’s new in AWS Glue 5.0 AWS Gl…

In today’s data-driven world, organizations are constantly seeking efficient ways to process and analyze vast amounts of information across data lakes and warehouses. Enter Amazon SageMaker Lakehouse, which you can use to unify all your data across Amazon Simple Storage Service (Amazon S3) data lakes and Amazon Redshift data warehouses, helping you build powerful analytics and AI and machine learning (AI/ML) applications on a single copy of data. SageMaker Lakehouse gives you the flexibility to access and query your data in-place with all Apache Iceberg compatible tools and engines. This opens up exciting possibilities for Open Source Apache Spark users who want to use …

Amazon SageMaker Unified Studio (preview) provides an integrated data and AI development environment within Amazon SageMaker. From the Unified Studio, you can collaborate and build faster using familiar AWS tools for model development, generative AI, data processing, and SQL analytics. This experience includes visual ETL, a new visual interface that makes it simple for data engineers to author, run, and monitor extract, transform, load (ETL) data integration flow. You can use a simple visual interface to compose flows that move and transform data and run them on serverless compute. Additionally, you can choose to author your visual flows with English using generative AI p…

With the growing emphasis on data, organizations are constantly seeking more efficient and agile ways to integrate their data, especially from a wide variety of applications. While traditional extract, transform, and load (ETL) processes have long been a staple of data integration due to its flexibility, for common use cases such as replication and ingestion, they often prove time-consuming, complex, and less adaptable to the fast-changing demands of modern data architectures. In addition, organizations rely on an increasingly diverse array of digital systems, data fragmentation has become a significant challenge. Valuable information is often scattered across multiple …

Yesterday, we announced Amazon SageMaker Unified Studio (Preview), an integrated experience for all your data and AI and Amazon SageMaker Lakehouse to unify data – from Amazon Simple Storage Service (S3) to third-party sources such as Snowflake. We’re excited by how SageMaker Lakehouse helps break down data silos, but we also know customers don’t want to compromise on data governance or introduce security and compliance risks as they expand data access. With this new capability, data analysts can now securely access and query data stored outside S3 data lakes, including Amazon Redshift data warehouses and Amazon DynamoDB databases, all through a single, unified experien…

This week on the keynote stages at AWS re:Invent 2024, you heard from Matt Garman, CEO, AWS, and Swami Sivasubramanian, VP of AI and Data, AWS, speak about the next generation of Amazon SageMaker, the center for all of your data, analytics, and AI. The relationship between analytics and AI is rapidly evolving. Our customers are telling us that they are seeing their analytics and AI workloads increasingly converge around a lot of the same data, and this is changing how they are using analytics tools with their data. They aren’t using analytics and AI tools in isolation. They’re taking data they’ve historically used for analytics or business reporting and putting it to wo…

In today’s rapidly evolving financial landscape, data is the bedrock of innovation, enhancing customer and employee experiences and securing a competitive edge. Recognizing this paradigm shift, ANZ Institutional Division has embarked on a transformative journey to redefine its approach to data management, utilization, and extracting significant business value from data insights. Like many large financial institutions, ANZ Institutional Division operated with siloed data practices and centralized data management teams. As time went on, the limitations of this approach became apparent due to rising data complexity, larger volumes, and the growing demand for swift, busines…

Today, AWS Partner Central announces the preview of Partner Connections, a new feature allowing AWS Partners to discover and connect with other Partners for collaboration on shared customer opportunities. With Partner Connections, Partners can co-sell joint solutions, accelerate deal progression, and expand their reach by teaming with other AWS Partners. At the core of Partner Connections are two key capabilities: connections discovery and multi-partner opportunities. The connections discovery feature uses AI-powered recommendations to streamline Partner matchmaking, making it easier for Partners to find suitable collaborators and add them to their network. With multi-…

Introducing a new AI Security category in the AWS Security competency to help customers easily identify AWS Partners with deep experience securing AI environments, and defending AI workloads against advanced threats and attacks. Partners in this new category are validated for their capabilities in areas like prevention of sensitive data disclosure, prevention of injection attacks, security posture management, implementing responsible AI filtering, and more. The rapid adoption of AI, and especially generative AI is transforming how customers build applications, but also introduces new security risks that require specialized expertise. Customers need solutions that can s…

Digital sovereignty has been a priority for AWS since its inception. AWS remains committed to offering customers the most advanced sovereignty controls and features in the cloud. With the increasing importance of digital sovereignty for public sector organizations and regulated industries, AWS is excited to announce the launch of the AWS Digital Sovereignty Competency. The AWS Digital Sovereignty Competency curates and validates a community of AWS Partners with advanced sovereignty capabilities and solutions, including deep experience in helping customers address sovereignty and compliance requirements. These partners can assist customers with residency control, access…

Today, AWS Security Incident Response launches a new AWS Specialization with approved partners from the AWS Partner Network (APN). AWS customers today rely on various 3rd party tools and services to support their internal security incident response capabilities. To better help both customers and partners, AWS introduced AWS Security Incident Response, a new service that helps customers prepare for, respond to, and recover from security events. Alongside approved AWS Partners, AWS Security Incident Response monitors, investigates, and escalates triaged security findings from Amazon GuardDuty and other threat detection tools through AWS Security Hub. Security Incident Respo…

We are excited to announce the new Amazon Security Lake Ready Specialization, which recognizes AWS Partners who have technically validated their software solutions to integrate with Amazon Security Lake and demonstrated successful customer deployments. These solutions have been technically validated by AWS Partner Solutions Architects for their sound architecture and proven customer success. Security Lake Ready software solutions can either contribute data to the Security Lake or consume this data and provide analytics, delivering a cohesive security solution for AWS customers. Amazon Security Lake automates data management tasks for customers, reducing costs and conso…

Today, AWS Marketplace announces Buy with AWS, a new feature that helps accelerate discovery and procurement on AWS Partners’ websites for products available in AWS Marketplace. Partners that sell or resell products in AWS Marketplace can now offer new experiences on their websites that are powered by AWS Marketplace. Customers can more quickly identify solutions from Partners that are available in AWS Marketplace and use their AWS accounts to access a streamlined purchasing experience. Customers browsing on Partner websites can explore products that are “Available in AWS Marketplace” and request demos, access free trials, and request custom pricing. Customers can conv…

Amazon Bedrock Intelligent Prompt Routing routes prompts to different foundational models within a model family, helping you optimize for quality of responses and cost. Using advanced prompt matching and model understanding techniques, Intelligent Prompt Routing predicts the performance of each model for each request and dynamically routes each request to the model that it predicts is most likely to give the desired response at the lowest cost. Customers can choose from two prompt routers in preview that route requests either between Claude Sonnet 3.5 and Claude Haiku, or between Llama 3.1 8B and Llama 3.1 70B. Amazon Bedrock is a fully managed service that offers a ch…

Amazon Bedrock Knowledge Bases now enables developers to build generative AI applications that can analyze and leverage insights from both textual and visual data, such as images, charts, diagrams, and tables. Bedrock Knowledge Bases offers end-to-end managed Retrieval-Augmented Generation (RAG) workflow that enables customers to create highly accurate, low-latency, secure, and custom generative AI applications by incorporating contextual information from their own data sources. With this launch, Bedrock Knowledge Bases extracts content from both text and visual data, generates semantic embeddings using the selected embedding model, and stores them in the chosen vector st…

A new scenario analysis capability of Amazon Q in QuickSight is now available in preview. This new capability provides an AI-assisted data analysis experience that helps you make better decisions, faster. Amazon Q in QuickSight simplifies in-depth analysis with step-by-step guidance, saving hours of manual data manipulation and unlocking data-driven decision-making across your organization. Amazon Q in QuickSight helps business users perform complex scenario analysis up to 10x faster than spreadsheets. You can ask a question or state your goal in natural language and Amazon Q in QuickSight guides you through every step of advanced data analysis—suggesting analytical app…

Amazon SageMaker HyperPod recipes help you get started training and fine-tuning publicly available foundation models (FMs) in minutes with state-of-the-art performance. SageMaker HyperPod helps customers scale generative AI model development across hundreds or thousands of AI accelerators with built-in resiliency and performance optimizations, decreasing model training time by up to 40%. However, as FM sizes continue to grow to hundreds of billions of parameters, the process of customizing these models can take weeks of extensive experimenting and debugging. In addition, performing training optimizations to unlock better price performance is often unfeasible for customers…

Amazon Kendra is an AI-powered search service enabling organizations to build intelligent search experiences and retrieval augmented generation (RAG) systems to power generative AI applications. Starting today, AWS customers can use a new index - the GenAI Index for RAG and intelligent search. With the Kendra GenAI Index, customers get high out-of-the-box search accuracy powered by the latest information retrieval technologies and semantic models. Kendra GenAI Index supports mobility across AWS generative AI services like Amazon Bedrock Knowledge Base and Amazon Q Business, giving customers the flexibility to use their indexed content across different use cases. It is …

Today, we are announcing the support of GraphRAG, a new capability in Amazon Bedrock Knowledge Bases that enhances Generative AI applications by providing more comprehensive, relevant and explainable responses using RAG techniques combined with graph data. Amazon Bedrock Knowledge Bases offers fully-managed, end-to-end Retrieval-Augmented Generation (RAG) workflows to create highly accurate, low latency, and custom Generative AI applications by incorporating contextual information from your company's data sources. Amazon Bedrock Knowledge Bases now offers a fully-managed GraphRAG capability with Amazon Neptune Analytics. Previously, customers faced challenges in conduc…

Starting today, you can build ML models using natural language with Amazon Q Developer, now available in Amazon SageMaker Canvas in preview. You can now get generative AI-powered assistance through the ML lifecycle, from data preparation to model deployment. With Amazon Q Developer, users of all skill levels can use natural language to access expert guidance to build high-quality ML models, accelerating innovation and time to market. Amazon Q Developer will break down your objective into specific ML tasks, define the appropriate ML problem type, and apply data preparation techniques to your data. Amazon Q Developer then guides you through the process of building, evalu…

Today, AWS announces that Amazon Bedrock now supports prompt caching. Prompt caching is a new capability that can reduce costs by up to 90% and latency by up to 85% for supported models by caching frequently used prompts across multiple API calls. It allows you to cache repetitive inputs and avoid reprocessing context, such as long system prompts and common examples that help guide the model’s response. When cache is used, fewer computing resources are needed to generate output. As a result, not only can we process your request faster, but we can also pass along the cost savings from using fewer resources. Amazon Bedrock is a fully managed service that offers a choice …

Organizations are increasingly using applications with multimodal data to drive business value, improve decision-making, and enhance customer experiences. Amazon Bedrock Guardrails now supports multimodal toxicity detection for image content, enabling organizations to apply content filters to images. This new capability with Guardrails, now in public preview, removes the heavy lifting required by customers to build their own safeguards for image data or spend cycles with manual evaluation that can be error-prone and tedious. Bedrock Guardrails helps customers build and scale their generative AI applications responsibly for a wide range of use cases across industry vert…

Today, we are announcing the preview launch of Amazon Bedrock Data Automation (BDA), a new feature of Amazon Bedrock that enables developers to automate the generation of valuable insights from unstructured multimodal content such as documents, images, video, and audio to build GenAI-based applications. These insights include video summaries of key moments, detection of inappropriate image content, automated analysis of complex documents, and much more. Developers can also customize BDA’s output to generate specific insights in consistent formats required by their systems and applications. By leveraging BDA, developers can reduce development time and effort, making it …

Today, AWS announces the availability of new AWS AI Service Cards for Amazon Nova Reel; Amazon Canvas; Amazon Nova Micro, Lite, and Pro; Amazon Titan Image Generator; and Amazon Titan Text Embeddings. AI Service Cards are a resource designed to enhance transparency by providing customers with a single place to find information on the intended use cases and limitations, responsible AI design choices, and performance optimization best practices for AWS AI services. AWS AI Service Cards are part of our comprehensive development process to build services in a responsible way. They focus on key aspects of AI development and deployment, including fairness, explainability, pr…

Amazon announces a five-year commitment of cloud technology and technical support for organizations creating digital learning solutions that expand access for underserved learners worldwide through the AWS Education Equity Initiative. While the use of educational technologies continues to rise, many organizations lack access to cloud computing and AI resources needed to accelerate and scale their work to reach more learners in need. Amazon is committing up to $100 million in AWS credits and technical advising to support socially-minded organizations build and scale learning solutions that utilize cloud and AI technologies. This will help reduce initial financial barrie…

Amazon Bedrock Marketplace provides generative AI developers access to over 100 publicly available and proprietary foundation models (FMs), in addition to Amazon Bedrock’s industry-leading, serverless models. Customers deploy these models onto SageMaker endpoints where they can select their desired number of instances and instance types. Amazon Bedrock Marketplace models can be accessed through Bedrock’s unified APIs, and models which are compatible with Bedrock’s Converse APIs can be used with Amazon Bedrock’s tools such as Agents, Knowledge Bases, and Guardrails. Amazon Bedrock Marketplace empowers generative AI developers to rapidly test and incorporate a diverse arr…

Amazon Bedrock Knowledge Bases now supports natural language querying to retrieve structured data from your data sources. With this launch, Bedrock Knowledge Bases offers an end-to-end managed workflow for customers to build custom generative AI applications that can access and incorporate contextual information from a variety of structured and unstructured data sources. Using advanced natural language processing, Bedrock Knowledge Bases can transform natural language queries into SQL queries, allowing users to retrieve data directly from the source without the need to move or preprocess the data. Developers often face challenges integrating structured data into genera…

Amazon SageMaker HyperPod announces flexible training plans, a new capability that allows you to train generative AI models within your timelines and budgets. Gain predictable model training timelines and run training workloads within your budget requirements, while continuing to benefit from features of SageMaker HyperPod such as resiliency, performance-optimized distributed training, and enhanced observability and monitoring. In a few quick steps, you can specify your preferred compute instances, desired amount of compute resources, duration of your workload, and preferred start date for your generative AI model training. SageMaker then helps you create the most cost…

Amazon SageMaker HyperPod now provides you with centralized governance across all generative AI development tasks, such as training and inference. You have full visibility and control over compute resource allocation, ensuring the most critical tasks are prioritized and maximizing compute resource utilization, reducing model development costs by up to 40%. With HyperPod task governance, administrators can more easily define priorities for different tasks and set up limits for how many compute resources each team can use. At any given time, administrators can also monitor and audit the tasks that are running or waiting for compute resources through a visual dashboard. W…

Today Amazon Web Services, Inc. (AWS) announced the general availability of Amazon SageMaker partner AI apps, a new capability that enables customers to easily discover, deploy, and use best-in-class machine learning (ML) and generative AI (GenAI) development applications from leading app providers privately and securely, all without leaving Amazon SageMaker AI so they can develop performant AI models faster. Until today, integrating purpose-built GenAI and ML development applications that provide specialized capabilities for a variety of model development tasks, required a considerable amount of effort. Beyond the need to invest time and effort in due diligence to eva…

The AWS Glue Data Catalog now automates generating statistics for new tables. These statistics are integrated with the cost-based optimizer (CBO) from Amazon Redshift Spectrum and Amazon Athena, resulting in improved query performance and potential cost savings. Queries on large datasets often read extensive amounts of data and perform complex join operations across multiple datasets. When a query engine like Redshift Spectrum or Athena processes the query, the CBO uses table statistics to optimize it. For example, if the CBO knows the number of distinct values in a table column, it can choose the optimal join order and strategy. These statistics must be collected befor…

This post is co-authored by Alex Kestner (Sr Product Manager, Amazon EKS), Ashley Ansari (Sr. Product Marketing Manager), Robert Northard (Principal GTM SSA Containers), and Sheetal Joshi (Principal Solution Architect, Containers). Introduction We announced general availability of Amazon Elastic Kubernetes Service (Amazon EKS) Auto Mode that provides a new capability streamlining Kubernetes cluster management for compute, storage, and networking. You can now get started quickly, improve performance, and reduce overhead, enabling you to focus on building applications that drive innovation by offloading cluster management to AWS. Amazon EKS Auto Mode streamlines Kubern…

Today, AWS announces Amazon SageMaker Data and AI Governance, a new capability that simplifies discovery, governance, and collaboration for data and AI across your lakehouse, AI models, and applications. Built on Amazon DataZone, SageMaker Data and AI Governance allows engineers, data scientists, and analysts to securely discover and access approved data and models using semantic search with generative AI–created metadata. This new offering helps organizations consistently define and enforce access policies using a single permission model with fine-grained access controls. With SageMaker Data and AI Governance, you can accelerate data and AI discovery and collaboration…

Today, AWS announces the public preview of the integration between Amazon Q Business and Amazon QuickSight, delivering a transformative capability that unifies answers from structured data sources (databases, warehouses) and unstructured data (documents, wikis, emails) in a single application. Amazon Q Business is a generative AI–powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in your enterprise systems. Amazon QuickSight is a business intelligence (BI) tool that helps you visualize and understand your structured data through interactive dashboards, reports, and analytics. While…

Today we are announcing the preview launch of Amazon Bedrock IDE, a governed collaborative environment integrated within Amazon SageMaker Unified Studio (preview) that enables developers to swiftly build and tailor generative AI applications. It provides an intuitive interface for developers across various skill levels to access Amazon Bedrock's high-performing foundation models (FMs) and advanced customization capabilities in order to collaboratively build custom generative AI applications. Amazon Bedrock IDE's integration into Amazon SageMaker Unified Studio removes barriers between data, tools, and builders, for generative AI development. Teams can now access their …

Starting today, Amazon Q Developer can document your code by automatically generating readme files and data-flow diagrams within your projects. Today, developers report they spend an average of just one hour per day coding. They spend most of their time on tedious, undifferentiated tasks such as learning codebases, writing and reviewing documentation, testing, managing deployments, troubleshooting issues or finding and fixing vulnerabilities. Q Developer is a generative AI-powered assistant for designing, building, testing, deploying, and maintaining software. Its agents for software development have a deep understanding of your entire code repos, so they can accelerat…

Today, AWS announces the next generation of Amazon SageMaker, including the preview launch of Amazon SageMaker Unified Studio, an integrated data and AI development environment that enables collaboration and helps teams build data products faster. SageMaker Unified Studio brings together familiar tools from AWS analytics and AI/ML services for data processing, SQL analytics, machine learning model development, and generative AI application development. Amazon SageMaker Lakehouse, which is accessible through SageMaker Unified Studio, provides open source compatibility and access to data stored across Amazon Simple Storage Service (Amazon S3) data lakes, Amazon Redshift dat…

Today, AWS announces a preview of GitLab Duo with Amazon Q, embedding advanced agent capabilities for software development and workload transformation directly in GitLab's enterprise DevSecOps platform. With this launch, GitLab Duo with Amazon Q delivers a seamless development experience across tasks and teams, automating complex, multi-step tasks for software development, security, and transformation —all using the familiar GitLab workflows developers already know. Using GitLab Duo, developers can delegate issues to Amazon Q agents using quick actions. to build new features faster, maximize quality and security with AI-assisted code reviews, create and execute unit te…

Now generally available, Amazon Q in QuickSight provides users with unified insights from structured and unstructured data sources through integration with Amazon Q Business. While structured data is managed in conventional systems, unstructured data such as document libraries, webpages, images and more has remained largely untapped due to its diverse and distributed nature. With Amazon Q in QuickSight business users can now augment insights from traditional BI data sources such as databases, data lakes and data warehouses, with contextual information from unstructured sources. Users can get augmented insights within QuickSight's BI interface across multi-visual Q&…

AWS announces general availability of Data Lineage in Amazon DataZone and next generation of Amazon SageMaker, a capability that automatically captures lineage from AWS Glue and Amazon Redshift to visualize lineage events from source to consumption. Being OpenLineage compatible, this feature allows data producers to augment the automated lineage with lineage events captured from OpenLineage-enabled systems or through API, to provide a comprehensive data movement view to data consumers. This feature automates lineage capture of schema and transformations of data assets and columns from AWS Glue, Amazon Redshift, and Spark executions in tools to maintain consistency and …

Amazon SageMaker Lakehouse and Amazon Redshift now support zero-ETL integrations from applications, automating the extraction and loading of data from eight applications, including Salesforce, SAP, ServiceNow, and Zendesk. As an open, unified, and secure lakehouse for your analytics and AI initiatives, Amazon SageMaker Lakehouse enhances these integrations to streamline your data management processes. These zero-ETL integrations are fully managed by AWS and minimize the need to build ETL data pipelines. With this new zero-ETL integration, you can efficiently extract and load valuable data from your customer support, relationship management, and ERP applications into you…

Today, we are excited to announce the general availability of AWS Glue 5.0. With AWS Glue 5.0, you get improved performance, enhanced security, support for Amazon Sagemaker Unified Studio and Sagemaker Lakehouse, and more. AWS Glue 5.0 enables you to develop, run, and scale your data integration workloads and get insights faster. AWS Glue is a serverless, scalable data integration service that makes it simple to discover, prepare, move, and integrate data from multiple sources. AWS Glue 5.0 upgrades the engines to Apache Spark 3.5.2, Python 3.11, and Java 17, with new performance and security improvements. Glue 5.0 updates open table format support to Apache Hudi 0.15.…

Amazon SageMaker now supports connectivity, discovery, querying, and enforcing fine-grained data access controls on federated sources when querying data with Amazon Athena. Athena is a query service that makes it simple to analyze your data lake and federated data sources such as Amazon Redshift, Amazon DynamoDB, or Snowflake using SQL without extract, transform, and load (ETL) scripts. Now, data workers can connect to and unify these data sources within SageMaker Lakehouse. Federated source metadata is unified in SageMaker Lakehouse, where you apply fine-grained policies in one place, helping to streamline analytics workflows and secure your data. Log into Amazon Sage…

Amazon S3 Access Grants now integrate with AWS Glue for analytics, machine learning (ML), and application development workloads in AWS. S3 Access Grants map identities from your Identity Provider (IdP), such as Entra ID and Okta or AWS Identity and Access Management (IAM) principals, to datasets stored in Amazon S3. This integration gives you the ability to manage S3 permissions for end users running jobs with Glue 5.0 or later, without the need to write and maintain bucket policies or individual IAM roles. AWS Glue provides a data integration service that simplifies data exploration, preparation, and integration from multiple sources, including S3. Using S3 Access Gra…

AWS Glue Data Catalog now automates generating statistics for new tables. These statistics are integrated with cost-based optimizer (CBO) from Amazon Redshift and Amazon Athena, resulting in improved query performance and potential cost savings. Table statistics are used by a query engine, such as Amazon Redshift and Amazon Athena, to determine the most efficient way to execute a query. Previously, creating statistics for Apache Iceberg tables in AWS Glue Data Catalog required you to continuously monitor and update configurations for your tables. Now, AWS Glue Data Catalog lets you generate statistics automatically for new tables with one time catalog configuration. Yo…

Today, AWS announces new generative AI–powered capabilities of Amazon Q Developer in public preview to help customers and partners accelerate large-scale assessment and modernization of mainframe applications. Amazon Q Developer is enterprise-ready, offering a unified web experience tailored for large-scale modernization, federated identity, and easier collaboration. Keeping you in the loop, Amazon Q Developer agents analyze and document your code base, identify missing assets, decompose monolithic applications into business domains, plan modernization waves, and refactor code. You can chat with Amazon Q Developer in natural language to share high-level transformation o…

Amazon DynamoDB zero-ETL integration with Amazon SageMaker Lakehouse automates the extracting and loading of data from a DynamoDB table into SageMaker Lakehouse, an open and secure lakehouse. You can run analytics and machine learning workloads on your DynamoDB data using SageMaker Lakehouse, without impacting production workloads running on DynamoDB. With this launch, you now have the option to enable analytics workloads using SageMaker Lakehouse, in addition to the previously available Amazon OpenSearch Service and Amazon Redshift zero-ETL integrations. Using the no-code interface, you can maintain an up-to-date replica of your DynamoDB data in the data lake by quickl…

Amazon SageMaker Lakehouse announces unified data connectivity capabilities to streamline the creation, management, and usage of connections to data sources across databases, data lakes and enterprise applications. SageMaker Lakehouse unified data connectivity provides a connection configuration template, support for standard authentication methods like basic authentication and OAuth 2.0, connection testing, metadata retrieval, and data preview. Customers can create SageMaker Lakehouse connections through SageMaker Unified Studio (preview), AWS Glue console, or custom-built application using APIs under AWS Glue. With SageMaker Lakehouse unified data connectivity, a dat…

Today, AWS announces new generative-AI powered transformation capabilities of Amazon Q Developer in public preview to accelerate porting of .NET Framework applications to cross-platform .NET. Using these capabilities, you can modernize your Windows .NET applications to be Linux-ready up to four times faster than traditional methods and realize up to 40% savings in licensing costs. With this launch, Amazon Q Developer is now equipped with agentic capabilities for transformation that allow you to port hundreds of .NET Frameworks applications running on Windows to Linux-ready cross-platform .NET. Using Amazon Q Developer, you can delegate your tedious manual porting tasks…

Today, Amazon Q Developer announces the general availability of a new agent that automates the end-to-end process of generating unit tests. This agent can be easily initiated by using a simple prompt: “/test”. Once prompted, Amazon Q will use the knowledge of your project to automatically generate and add tests to your project, helping improve code quality, fast. Amazon Q Developer will also ask you to provide consent before adding tests, allowing you to always stay in the loop so that no unintended changes are made. Automation saves the time and effort needed to write comprehensive unit tests, allowing you to focus on building innovative features. With the ability to …

Today, we are excited to announce that Amazon Q Business, including Amazon Q Apps, has expanded its capabilities with a ready-to-use library of over 50 actions spanning plugins across popular business applications and platforms. This enhancement allows Amazon Q Business users to complete tasks in other applications without leaving the Amazon Q Business interface, improving the user experience and operational efficiency. The new plugins cover a wide range of widely used business tools, including PagerDuty, Salesforce, Jira, Smartsheet, and ServiceNow. These integrations enable users to perform tasks such as creating and updating tickets, managing incidents, and accessin…

Amazon Q Developer now helps you accelerate operational investigations across your AWS environment in just a fraction of the time. With a deep understanding of your AWS cloud environment and resources, Amazon Q Developer looks for anomalies in your environment, surfaces related signals for you to explore, identifies potential root-cause hypothesis, and suggests next steps to help you remediate issues faster. Amazon Q Developer works alongside you throughout your operational troubleshooting journey from issue detection and triaging, through remediation. You can initiate an investigation by selecting the Investigate action on any Amazon CloudWatch data widget across the …

Starting today, Amazon Q Developer can also perform code reviews, automatically providing comments on your code in the IDE, flagging suspicious code patterns, providing patches where available, and even assessing deployment risk so you can get feedback on your code quickly. Q Developer is a generative AI-powered assistant for designing, building, testing, deploying, and maintaining software. Its agents for software development have a deep understanding of your entire code repos, so they can accelerate many tasks beyond coding. By automating the first round of code reviews and improving review consistency, Q Developer empowers code authors to fix issues faster, streamli…

AWS announces Amazon SageMaker Lakehouse, a unified, open, and secure data lakehouse that simplifies your analytics and artificial intelligence (AI). Amazon SageMaker Lakehouse unifies all your data across Amazon S3 data lakes and Amazon Redshift data warehouses, helping you build powerful analytics and AI/ML applications on a single copy of data. SageMaker Lakehouse gives you the flexibility to access and query your data in-place with Apache Iceberg open standard. All data in SageMaker Lakehouse can be queried from SageMaker Unified Studio (preview) and engines such as Amazon EMR, AWS Glue, Amazon Redshift or Apache Spark. You can secure your data in the lakehouse by …

Today, AWS announces the next generation of Amazon SageMaker, a unified platform for data, analytics, and AI. This launch brings together widely adopted AWS machine learning and analytics capabilities and provides an integrated experience for analytics and AI with unified access to data and built-in governance. Teams can collaborate and build faster from a single development environment using familiar AWS tools for model development, generative AI application development, data processing, and SQL analytics, accelerated by Amazon Q Developer, the most capable generative AI assistant for software development. The next generation of SageMaker also introduces new capabiliti…

We’re excited to announce Amazon Nova, a new generation of state-of-the-art (SOTA) foundation models (FMs) that deliver frontier intelligence and industry leading price performance. Amazon Nova models available today on Amazon Bedrock are: Amazon Nova Micro, a text only model that delivers the lowest latency responses at very low cost. Amazon Nova Lite, a very low-cost multimodal model that is lightning fast for processing image, video, and text inputs Amazon Nova Pro, a highly capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks. Amazon Nova Canvas, a state-of-the-art image generation model. Amazon Nova Re…

Today, AWS announces the preview of Amazon Aurora DSQL, a new serverless, distributed SQL database with active-active high availability. Aurora DSQL allows you to build always available applications with virtually unlimited scalability, the highest availability, and zero infrastructure management. It is designed to make scaling and resiliency effortless for your applications, and offers the fastest distributed SQL reads and writes. Aurora DSQL provides virtually unlimited horizontal scaling with the flexibility to independently scale reads, writes, compute, and storage. It automatically scales to meet any workload demand without database sharding or instance upgrades. I…

Amazon S3 Tables deliver the first cloud object store with built-in Apache Iceberg support, and the easiest way to store tabular data at scale. S3 Tables are specifically optimized for analytics workloads, resulting in up to 3x faster query throughput and up to 10x higher transactions per second compared to self-managed tables. With S3 Tables support for the Apache Iceberg standard, your tabular data can be easily queried by popular AWS and third-party query engines. Additionally, S3 Tables are designed to perform continual table maintenance to automatically optimize query efficiency and storage cost over time, even as your data lake scales and evolves. S3 Tables integrat…

Today, AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) Trn2 instances and preview of Trn2 UltraServers, powered by AWS Trainium2 chips. Available via EC2 Capacity Blocks, Trn2 instances and UltraServers are the most powerful EC2 compute solutions for deep learning and generative AI training and inference. You can use Trn2 instances to train and deploy the most demanding foundation models including large language models (LLMs), multi-modal models, diffusion transformers and more to build a broad set of AI applications. To reduce training times and deliver breakthrough response times (per-token-latency) for the most capable, state-of-th…

With the launch of the Automated Reasoning checks safeguard in Amazon Bedrock Guardrails, AWS becomes the first and only major cloud provider to integrate automated reasoning in our generative AI offerings. Automated Reasoning checks help detect hallucinations and provide a verifiable proof that a large language model (LLM) response is accurate. Automated Reasoning tools are not guessing or predicting accuracy. Instead, they rely on sound mathematical techniques to definitively verify compliance with expert-created Automated Reasoning Policies, consequently improving transparency. Organizations increasingly use LLMs to improve user experiences and reduce operational costs…

With Amazon Bedrock Model Distillation, customers can use smaller, faster, more cost-effective models that deliver use-case specific accuracy that is comparable to the most capable models in Amazon Bedrock. Today, fine-tuning a smaller cost-efficient model to increase its accuracy for a customers’ use-case is an iterative process where customers need to write prompts and response, refine the training dataset, ensure that the training dataset captures diverse examples, and adjust the training parameters. Amazon Bedrock Model Distillation automates the process needed to generate synthetic data from the teacher model, trains and evaluates the student model, and then ho…