Last week, I took some time off in York, England, often described as the most haunted city in the country. I wandered through the ruins of abbeys that have stood for nearly a thousand years, walked along medieval walls, and spent an evening on a ghost tour hearing stories passed down through centuries. There’s something grounding about standing in a place that has witnessed so much history. Now I’m back at my desk, and the contrast is hard to miss: those abbey stones have stood for a thousand years largely unchanged, while in the span of a single week away, the pace of technological change has moved forward yet again.

The ruins of Whitby Abbey in North Yorkshire. Stones that have seen a thousand years, while this week alone brought another wave of change.

Now, let’s get into this week’s AWS news.

Headlines

On April 28, Matt Garman, CEO of AWS, Colleen Aubrey, SVP Amazon Applied AI Solutions, Julia White, CMO of AWS, and OpenAI leaders took the stage to share how customers are changing the way businesses operate with agents. The event brought a packed slate of announcements across Amazon Quick, Amazon Connect, and a deeper partnership with OpenAI. Here’s a roundup of the biggest announcements from the event.

Amazon Quick expands with a desktop app, new pricing plans, and visual asset generation – Amazon Quick is an AI assistant for work that connects to your apps, learns what matters to you, and takes action on your behalf. This week, Quick introduced a new desktop app (Preview) that keeps you connected to your local files, calendar, and communications without opening a browser. You can sign up within minutes using your personal email address or existing Google, Apple, Github, or Amazon credentials—no AWS account required. Quick can now generate polished documents, presentations, infographics, and images directly from the chat interface, and native integrations expand to include Google Workspace, Zoom, Airtable, Dropbox, and Microsoft Teams. A new Build custom apps with Quick capability (Preview) lets you create intelligent apps, dashboards, and web pages connected to the rest of your business using natural language.

Amazon Connect expands into four agentic AI solutions – Amazon Connect is expanding from a single product into a set of four agentic AI solutions designed to work within your existing workflows. Amazon Connect Decisions is a supply chain planning and intelligence solution that shifts teams from crisis management to proactive planning, combining 30 years of Amazon operational science with more than 25 specialized supply chain tools. Amazon Connect Talent (Preview) is an agentic AI hiring solution that delivers AI-led interviews, science-backed assessments, and consistent evaluation for talent acquisition leaders managing scaled hiring. Amazon Connect Customer, previously known as Amazon Connect, delivers personalized customer experiences across voice, chat, and digital channels, with new configuration capabilities that enable organizations to set up conversational AI in weeks rather than months. Amazon Connect Health delivers agentic patient verification, appointment management, patient insights, ambient documentation, and medical coding, giving patients faster access to care and clinicians more time to deliver it.

AWS and OpenAI expand their partnership across Amazon Bedrock – AWS and OpenAI are bringing the latest OpenAI models to Amazon Bedrock, launching Codex on Amazon Bedrock, and introducing Amazon Bedrock Managed Agents powered by OpenAI — all in limited preview. OpenAI models on Amazon Bedrock (Limited preview) brings the latest OpenAI models, including GPT-5.5 and GPT-5.4, to the Bedrock APIs you already use, with unified security, governance, and cost controls. No additional infrastructure to configure, no new security model to learn. Codex on Amazon Bedrock (Limited preview) lets you access the OpenAI coding agent within your existing AWS environments, authenticating with your AWS credentials, processing inference through Bedrock, and applying Codex usage toward your AWS cloud commitments. Codex on Bedrock is available through the Bedrock API, starting with the Codex CLI, the Codex desktop app, and a Visual Studio Code extension. Amazon Bedrock Managed Agents, powered by OpenAI (Limited preview) combines OpenAI frontier models with AWS infrastructure to build production-ready OpenAI-powered agents in the cloud, built with the OpenAI harness for faster execution, sharper reasoning, and reliable steering of long-running tasks.

To learn more, visit Top announcements of the What’s Next with AWS, 2026.

Last week’s launches

Here are some launches and updates from this past week that caught my attention:

• Amazon EC2 M8in and M8ib instances are now generally available – Powered by custom 6th-gen Intel Xeon Scalable processors and 6th-gen AWS Nitro cards, these instances deliver up to 43% higher performance over M6in and M6ib. M8in offers 600 Gbps network bandwidth, while M8ib delivers up to 300 Gbps EBS bandwidth. Available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Spain).
• Amazon EC2 R8in and R8ib instances are now generally available – Memory-optimized instances built on the same 6th-gen Intel Xeon Scalable processors and Nitro cards, with the same 600 Gbps network and 300 Gbps EBS bandwidth profiles. Well-suited for large commercial databases, data lakes, and in-memory databases such as SAP HANA. Available in US East (N. Virginia, Ohio), US West (Oregon), and Europe (Spain).
• Amazon EC2 C8ine and M8ine instances are now generally available – Network-optimized instances offering up to 2.5x higher packet performance per vCPU and up to 2x higher network throughput for traffic through internet gateways compared to C6in and M6in. Designed for security and network virtual appliances including virtual firewalls, load balancers, and 5G UPF workloads. Available in US East (N. Virginia), US West (Oregon), and Asia Pacific (Tokyo) for C8ine; US East (N. Virginia) and US West (Oregon) for M8ine.
• Amazon Bedrock AgentCore adds optimization capabilities (Preview) – AgentCore now offers recommendations, batch evaluations, and A/B tests to complete the observe-evaluate-improve loop for agents in production. Recommendations analyze production traces and evaluation outputs to propose optimized system prompts and tool descriptions, which you can validate with batch evaluations against pre-defined test cases or A/B tests against live traffic. Every recommendation requires your approval before it ships.
• AWS Lambda adds support for Ruby 4.0 – Ruby 4.0, the latest LTS release, is available as a Lambda managed runtime and container base image. It includes support for Lambda advanced logging controls, including JSON structured logs, configurable logging levels, and target CloudWatch log group configuration. Available in all AWS Regions, including China Regions and AWS GovCloud (US).

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news

Here are some additional posts and resources that you might find interesting:

• Amazon Q Developer end-of-support announcement – Amazon Q Developer IDE plugins and paid subscriptions will reach end of support on April 30, 2027, giving customers 12 months to transition to Kiro. New signups will be blocked starting May 15, 2026, although existing subscriptions can continue to add users. Starting May 29, 2026, Opus 4.6 will no longer be available on Q Developer Pro; Opus 4.5 and other existing models remain available, and the latest coding models including Opus 4.7 are available exclusively on Kiro. Amazon Q Developer in the AWS Management Console and first-party AWS experiences (documentation, mobile app, Slack, and Microsoft Teams) are not affected.
• AWS 10,000 AIdeas Competition: Meet the Winners – AWS announced the 20 winners of the 10,000 AIdeas Competition, a global challenge where builders submitted AI applications built entirely with Kiro and the AWS Free Tier, with submissions from 115 countries narrowed down through four rounds of evaluation and two rounds of community voting. Winners span Global Champions, Regional Champions, Innovation Awards, and Creative Track categories, with cash prizes and AWS credits awarded across each tier.
• AWS Student Builder Groups – AWS Cloud Clubs is evolving to AWS Student Builder Groups. The community now spans 600+ colleges and universities across 63 countries. Existing Cloud Club memberships, badges, and progress carry forward, and Cloud Club Captains become Group Leaders. Membership is open to any learner 18 or older. You can find a group near you on AWS Builder Center or apply to launch a new group on your campus.

Upcoming AWS events

Check your calendar and sign up for upcoming AWS events:

• AWS Summits – AWS Summits are free in-person events covering cloud and AI. Coming up in May: Singapore (May 6), Tel Aviv (May 6), Warsaw (May 6), Stockholm (May 7), Sydney (May 13–14), Hamburg (May 20), Seoul (May 20), Amsterdam (May 27), Milano (May 28), and Mumbai (May 28).
• AWS Community Days – Community-led conferences planned and delivered by community leaders. Upcoming events include İstanbul, Türkiye (May 9) and Panama City, Panama (May 23).

Visit the AWS Builder Center to meet other builders, contribute solutions, and find resources that help you keep building. You can also browse upcoming AWS-led in-person and virtual events, plus developer-focused sessions.

— Esra

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

The post AWS Weekly Roundup: What’s Next with AWS 2026, Amazon Quick, OpenAI partnership, and more (May 4, 2026) appeared first on The NOKIA Blog.

Last week I had the honor of delivering a commencement speech at the University of Namur (uNamur) for their 2025 graduation ceremony.

Standing in front of freshly minted computer science graduates, I talked about the future of software development in the age of AI. My message to them was simple: AI will not make you obsolete. We’ve seen tools evolve over the decades, from punch cards to IDEs to AI-assisted coding, but the work remains yours, not the tool’s. The developers who will thrive are those who stay curious, think in systems, communicate with precision, and take ownership of what they build. The world needs more people with coding skills, not fewer. AI raises the bar on what we can accomplish, and that’s a good thing.

Now, let’s get into this week’s AWS news.

Headlines

Anthropic’s Claude Opus 4.7 is now available in Amazon Bedrock – Anthropic’s most intelligent Opus model is now available in Amazon Bedrock, with improved performance across coding, long-running agents, and professional knowledge work. Claude Opus 4.7 scores 64.3% on SWE-bench Pro and 87.6% on SWE-bench Verified, extending its lead in agentic coding with stronger long-horizon autonomy and complex code reasoning. It also does better on knowledge work tasks like document creation, financial analysis, and multi-step research.

The model runs on Bedrock’s next-generation inference engine with dynamic capacity allocation, adaptive thinking (letting Claude allocate thinking token budgets based on request complexity), and the full 1M token context window. It also adds high-resolution image support for better accuracy on charts, dense documents, and screen UIs. Claude Opus 4.7 is available at launch in US East (N. Virginia), Asia Pacific (Tokyo), Europe (Ireland), and Europe (Stockholm), with up to 10,000 requests per minute per account per Region.

AWS Interconnect is now generally available with a new option to simplify last-mile connectivity – AWS Interconnect brings two managed private connectivity capabilities to general availability. The first, AWS Interconnect – Multicloud, provides Layer 3 private connections between AWS VPCs and other cloud providers (Google Cloud available now, Azure and OCI coming later in 2026). Traffic flows over the AWS global backbone and the partner cloud’s private network, never over the public internet, with built-in MACsec encryption, multi-facility resiliency, and CloudWatch monitoring. AWS published the underlying specification on GitHub under Apache 2.0 so any cloud provider can become an Interconnect partner.

The second capability, AWS Interconnect – Last Mile, simplifies high-speed private connections from branch offices, data centers, and remote locations to AWS through existing network providers. It provisions 4 redundant connections across 2 physical locations automatically, configures BGP routing, activates MACsec encryption and Jumbo Frames by default, and offers bandwidth from 1 Gbps to 100 Gbps adjustable from the console without reprovisioning. Last Mile launches in US East (N. Virginia) with Lumen as the initial partner.

Last week’s launches

Here are some launches and updates from this past week that caught my attention:

• Amazon ECR pull through cache now supports referrer discovery and sync — ECR’s pull through cache now automatically discovers and syncs OCI referrers (image signatures, SBOMs, attestations) from upstream registries into your private repositories. This means end-to-end image signature verification and SBOM discovery workflows work without client-side workarounds.
• AWS Transform is now available in Kiro and VS Code — AWS Transform, the agentic migration and modernization factory, is now accessible via Kiro (as a Power) and VS Code (as an extension). You can run custom transformations for common patterns like Java/Python/Node.js version upgrades and AWS SDK migrations directly from your IDE, with job state shared across the web console, CLI, and IDE.
• Aurora DSQL launches connector for PHP — A new Aurora DSQL Connector for PHP (PDO_PGSQL) simplifies building PHP applications on Aurora DSQL by automatically generating IAM tokens, handling SSL configuration, managing connection pooling, and providing opt-in optimistic concurrency control retry with exponential backoff.
• Amazon Q supports document-level access controls for Google Drive — Amazon Q now enforces document-level access controls for Google Drive knowledge bases, combining indexed ACL replication for fast pre-retrieval filtering with real-time permission checks against Google Drive at query time.
• AWS Secrets Manager now supports hybrid post-quantum TLS — Secrets Manager now supports hybrid post-quantum key exchange using ML-KEM to protect secrets against both current and future quantum computing threats. This is automatically enabled in Secrets Manager Agent 2.0.0+, Lambda Extension v19+, and the CSI Driver 2.0.0+.
• Amazon EC2 C8in and C8ib instances are now generally available — Powered by custom 6th-gen Intel Xeon Scalable processors and 6th-gen AWS Nitro cards, these instances deliver up to 43% higher performance over C6in. C8in offers 600 Gbps network bandwidth (highest among enhanced networking EC2 instances), while C8ib delivers up to 300 Gbps EBS bandwidth (highest among non-accelerated compute instances), scaling up to 384 vCPUs.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news

Here are some additional posts and resources that you might find interesting:

• Navigating enterprise networking challenges with Amazon EKS Auto Mode — This post explains how EKS Auto Mode automates Kubernetes networking infrastructure including VPC CNI configuration, load balancer provisioning, and DNS management, reducing operational overhead while preserving enterprise security controls.
• Introducing granular cost attribution for Amazon Bedrock — My colleague Micah talked about this feature last week already, but the blog post came out after last week’s roundup. Amazon Bedrock now automatically attributes inference costs to the specific IAM principal that made each API call, with results flowing into AWS Cost and Usage Reports (CUR 2.0). You can aggregate costs by team, project, or cost center using IAM principal tags and session tags.
• Accelerate development workflows with Amazon EBS Volume Clones — EBS Volume Clones let you create instant, point-in-time copies of EBS volumes that are immediately usable without waiting for data transfer. The post highlights use cases including dev/test environment refreshes, disaster recovery testing, and CI/CD pipeline acceleration.
• Modernize VB6 applications at scale with AWS Transform Custom — A walkthrough of using AWS Transform Custom’s agentic AI capabilities to convert legacy Visual Basic 6.0 applications to modern C# ASP.NET Core web applications, addressing challenges like COM dependencies, ADO-to-Entity Framework migration, and VB6 forms-to-Blazor UI conversion.
• Migrating and decomposing APIs with zero-downtime using CloudFront — A zero-downtime API migration strategy using CloudFront Functions with CloudFront KeyValueStore for intelligent, user-aware traffic routing based on the Strangler Fig pattern. This is similar to what AWS Migration Hub Refactor Spaces offers, but implemented with just CloudFront and edge functions.

Upcoming AWS events

Check your calendar and sign up for upcoming AWS events:

• AWS Events — Browse upcoming AWS-led in-person and virtual events, startup events, and developer-focused events near you.
• AWS Power Hour — Weekly live training sessions on Twitch covering various AWS topics.
• Community.aws — Find community-led events, meetups, and user groups in your area.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— seb

The post AWS Weekly Roundup: Claude Opus 4.7 in Amazon Bedrock, AWS Interconnect GA, and more (April 20, 2026) appeared first on The NOKIA Blog.

In August 2025, we introduced AWS User Experience Customization (UXC) capability to tailor user interfaces (UIs) to meet your specific needs and complete your tasks efficiently. With this capability, your account administrator can customize some UI component of AWS Management Console, such as assigning a color to an AWS account for easier identification.

Today, we’re announcing additional customization capability in UXC that enables selective display of relevant AWS Regions and services for your team members. By hiding unused Regions and services, you can reduce cognitive load and eliminate unnecessary clicks and scrolling, helping you focus better and work faster. With this launch, we offer the ability to customize account color, Region, and service visibility together.

Categorize account by color

You can set a color for your accounts to visually distinguish between them. To get started, sign in to the AWS Management Console and choose your account name on the navigation bar. Your account color isn’t set yet. To set the color, choose Account.

In the Account display settings, select your preferred account color and choose Update. You can see the chosen color in the navigation bar.

By changing the account color, you can clearly distinguish the account’s purpose. For example, you can use orange for development accounts, light blue for test accounts, and red for production accounts.

Customize Regions and services visibility

You can control which AWS Regions appear in the Region selector or which AWS services appear in the console navigation. In other words, you can set to show only the Regions and services that are relevant to your account.

To get started, choose the gear icon on the navigation bar and choose See all user settings. If you are in an administrator role, you can see a new Account settings tab in the unified settings. If you have not configured a setting, all Regions and services are visible.

To set visible Regions, choose Edit in the Visible Regions section. Select your visible Regions to All available Regions or Select Regions and configure your list. Choose Save changes.

After configuring visible Region setting, you will find only selected Regions in the Regions selector on the navigation bar in the console.

You can also set visible services in the same way. Search or select services from the category. I used the Popular services category to select my favorites. When you finish selection, choose Save changes.

After configuring visible services setting, you will find only selected services in the All services menu on the navigation bar.

When you search the service name in the search bar, you can only choose selected services.

The Regions and services visibility settings control only the appearance of services and Regions in the console. They don’t restrict access through the AWS Command Line Interface (AWS CLI), AWS SDKs, AWS APIs, or Amazon Q Developer.

You can also manage these account customization settings programmatically with new visibleServices and visibleRegions parameters. For example, you can use AWS CloudFormation sample template:

AWSTemplateFormatVersion: "2010-09-09"
Description: Customize AWS Console appearance for this account

Resources:
  AccountCustomization:
    Type: AWS::UXC::AccountCustomization
    Properties:
      AccountColor: red
      VisibleServices:
        - s3
        - ec2
        - lambda
      VisibleRegions:
        - us-east-1
        - us-west-2

And you can deploy your Cloudformation template.

$ aws cloudformation deploy 
  --template-file account-customization.yaml 
  --stack-name my-account-customization

To learn more, visit the AWS User Experience Customization API Reference and AWS CloudFormation template reference.

Give it a try in the AWS Management Console today and provide feedback by selecting the Feedback link at the bottom of the console, posting to the AWS re:Post forum for the AWS Management Console, or reaching out to your AWS Support contacts.

— Channy

The post Customize your AWS Management Console experience with visual settings including account color, region and service visibility appeared first on The NOKIA Blog.

AWS has reached its 20th anniversary! With a steady pace of innovation, AWS has grown to offer over 240 comprehensive cloud services and continues to launch thousands of new features annually for millions of customers. During this time, over 4,700 posts have been published on this blog—more than double the number since Jeff Barr wrote the 10th anniversary post.

AWS changed my life

Reflecting on what I was doing 20 years ago, I met Jeff in Seoul on March 13, 2006, when he came as the keynote speaker for the Korea NGWeb conference. At that time, Amazon was one of the first pioneers to initiate an API economy, introducing ecommerce API services. After the keynote speech, he returned home that evening, and I believe he wrote the Amazon S3 launch blog post on the flight back to the United States.

That short meeting with him brought significant changes to my life. He became my role model as a blogger, and I began building API-based services in my company and opening them to third-party developers. When I was a PhD student while taking a break from work, I realized that for individual researchers like me, AWS Cloud services are powerful tools for conducting large-scale research projects. After returning to work, my company became one of the first AWS customers in Korea in 2014. Countless developers—myself included—have embraced cloud computing and actively used its capabilities to accomplish what was previously impossible.

Over the past decade, the technology landscape has transformed dramatically. Deep learning emerged as a breakthrough in AI, evolving through generative AI based on large language models (LLMs) to today’s agentic AI technology. Jeff wrote, “When looking into the future, you need to be able to distinguish between flashy distractions and genuine trends, while remaining flexible enough to pivot if yesterday’s niche becomes today’s mainstream technology.” This principle guides how AWS approaches innovation—we start by listening to what customers truly need. The real trend isn’t pursuing every emerging technology, but rather reimagining solutions that address customers’ most critical challenges.

20 years of AWS

For the first 10 years, Jeff selected his favorite AWS launches and blog posts. Amazon S3, Amazon EC2 (2006), Amazon Relational Database Service, Amazon Virtual Private Cloud (2009), Amazon DynamoDB, Amazon Redshift (2012), Amazon WorkSpaces, Amazon Kinesis (2013), AWS Lambda (2014), and AWS IoT (2015).

While I also hate to play favorites, I want to choose some of my favorite AWS blog posts of the past decade.

• Deploying containers easily (2014) – Amazon Elastic Container Service makes it straightforward for you to run any number of containers across a managed cluster of Amazon EC2 instances using powerful APIs and other tools. In 2017, we launched Amazon Elastic Kubernetes Service as a fully managed Kubernetes service and AWS Fargate as a serverless deployment option.
• High availability database at global scale (2017) – Amazon Aurora is a modern relational database service offering performance and high availability at scale. In 2018, we launched Amazon Aurora Serverless v1, and this serverless database evolved to Amazon Aurora Serverless v2 to scale down to zero. In 2025, we also launched Amazon Aurora DSQL is the fastest serverless distributed SQL database for always available applications.
• Machine learning (ML) at your fingertips (2017) – Amazon SageMaker is a fully managed end-to-end ML service that data scientists, developers, and ML experts can use to quickly build, train, and host machine learning models at scale. In 2024, we launched the next generation of Amazon SageMaker, a unified platform for data, analytics, and AI and introduced Amazon SageMaker AI to focus specifically on building, training, and deploying AI and ML models at scale.
• Best price performance for cloud workloads (2018) – We launched Amazon EC2 A1 instances powered by the first generation of Arm-based AWS Graviton Processors designed to deliver the best price performance for your cloud workloads. Last year, we previewed EC2 M9g instances powered by AWS Graviton5 processors. Over 90,000 AWS customers have reaped the benefits of Graviton supporting popular AWS services such as Amazon ECS and Amazon EKS, AWS Lambda, Amazon RDS, Amazon ElastiCache, Amazon EMR, and Amazon OpenSearch Service.
• Run AWS Cloud in your data center (2019) – AWS Outposts is a family of fully managed services delivering AWS infrastructure and services to virtually any on-premises or edge location for a truly consistent hybrid experience. Now, AWS Outposts is available in a variety of form factors, from 1U and 2U Outposts servers to 42U Outposts racks, and multiple rack deployments. Customers such as DISH, Fanduel, Morningstar, Philips, and others use Outposts in workloads requiring low latency access to on-premises systems, local data processing, data residency, and application migration with local system interdependencies.
• Best price performance for ML workloads (2019) – We launched Amazon EC2 Inf1 instances powered by the first generation of AWS Inferentia chips designed to provide fast, low-latency inferencing. In 2022, we launched Amazon EC2 Trn1 instances powered by the first generation of AWS Trainium chips optimized for high performance AI training. Last year, we launched Amazon EC2 Trn3 UltraServers powered by Trainium3 to deliver the best token economics for next-generation generative AI applications. Customers such as Anthropic, Decart, poolside, Databricks, Ricoh, Karakuri, SplashMusic, and others are realizing performance and cost benefits of Trainium-based instances and UltraServers.
• Build your generative AI apps on AWS (2023) – Amazon Bedrock is a fully managed service that offers a choice of industry leading AI models along with a broad set of capabilities that you need to build generative AI applications, simplifying development with security, privacy, and responsible AI. Last year, we introduced Amazon Bedrock AgentCore, an agentic platform for building, deploying, and operating effective agents securely at scale. Now, more than 100,000 customers worldwide choose Amazon Bedrock to deliver personalized experiences, automate complex workflows, and uncover actionable insights.
• Your AI coding companion (2023) – We launched Amazon CodeWhisperer as the industry’s first cloud-based AI coding assistant service. The service delivered code generation from comments, open-source code reference tracking, and vulnerability scanning capabilities. In 2024, we rebranded the service to Amazon Q Developer and expanded its features to include a chat-based assistant in the console, project-based code generation, and code transformation tools. In 2025, this service evolved into Kiro, a new agentic AI development tool that brings structure to AI coding through spec-driven development, taking projects from prototype to production. Recently, Kiro previewed an autonomous agent, a frontier agent that works independently on development tasks, maintaining context and learning from every interaction.
• Broaden your AI model choices (2024) – We launched Amazon Titan models further increasing cost-effective AI model choice for text and multimodal needs in Amazon Bedrock. At AWS re:Invent 2024, we announced Amazon Nova models that delivers frontier intelligence and industry leading price performance. Now Amazon Nova has a portfolio of AI offerings—including Amazon Nova models, Amazon Nova Forge, a new service to build your own frontier models; and Amazon Nova Act, a new service to build agents that automate browser-based UI workflows powered by a custom Amazon Nova 2 Lite model.

Build with AI: Your path forward

A decade ago, AWS responded to the emergence of deep learning by launching the broadest and deepest ML services, such as Amazon SageMaker, democratizing AI for a wide range of customers—from individual developers and startups to large enterprises—regardless of their technical expertise.

AI technology has advanced significantly, but building and deploying AI models and applications still remains complex for many developers and organizations. AWS offers the broadest selection of AI models through Amazon Bedrock, including leading providers such as Anthropic and OpenAI. By using our model training and inference infrastructure and responsible AI both practical and scalable, you can accelerate trusted AI innovation while maintaining control of your data and costs—all built on our global infrastructure’s operational excellence.

Reinvent your idea, keep on learning, build confidently with AI you can trust, and share your successes with us! New AWS customers receive up to $200 in credits to try AWS AI for free. If you’re a student, start building with Kiro for free using 1,000 credits per month for one year.

— Channy

The post 20 years in the AWS Cloud – how time flies! appeared first on The NOKIA Blog.

Here are the notable launches and updates from last week that can help you build, scale, and innovate on AWS.

Last week’s launches

Here are the launches that got my attention this week.

Let’s start with news related to compute and networking infrastructure:

• Introducing Amazon EC2 C8id, M8id, and R8id instances: These new Amazon EC2 C8id, M8id, and R8id instances are powered by custom Intel Xeon 6 processors. These instances offer up to 43% higher performance and 3.3x more memory bandwidth compared to previous generation instances.
• AWS Network Firewall announces new price reductions: The service has added the hourly and data processing discounts on NAT Gateways that are service-chained with Network Firewall secondary endpoints. Additionally, AWS Network Firewall has removed additional data processing charges for Advanced Inspection, which enables Transport Layer Security (TLS) inspection of encrypted network traffic.
• Amazon ECS adds Network Load Balancer support for Linear and Canary deployments: Applications that commonly use NLB, such as those requiring TCP/UDP-based connections, low latency, long-lived connections, or static IP addresses, can take advantage of managed, incremental traffic shifting natively from ECS when rolling out updates.
• AWS Config now supports 30 new resource types: These range across key services including Amazon EKS, Amazon Q, and AWS IoT. This expansion provides greater coverage over your AWS environment, enabling you to more effectively discover, assess, audit, and remediate an even broader range of resources.
• Amazon DynamoDB global tables now support replication across multiple AWS accounts: DynamoDB global tables are a fully managed, serverless, multi-Region, and multi-active database. With this new capability, you can replicate tables across AWS accounts and Regions to improve resiliency, isolate workloads at the account level, and apply distinct security and governance controls.
• Amazon RDS now provides an enhanced console experience to connect to a database: The new console experience provides ready-made code snippets for Java, Python, Node.js, and other programming languages as well as tools like the psql command line utility. These code snippets are automatically adjusted based on your database’s authentication settings. For example, if your cluster uses IAM authentication, the generated code snippets will use token-based authentication to connect to the database. The console experience also includes integrated CloudShell access, offering the ability to connect to your databases directly from within the RDS console.

Then, I noticed three news items related to security and how you authenticate on AWS:

• AWS Builder ID now supports Sign in with Apple: AWS Builder ID, your profile for accessing AWS applications including AWS Builder Center, AWS Training and Certification, AWS re:Post, AWS Startups, and Kiro, now supports sign-in with Apple as a social login provider. This expansion of sign-in options builds on the existing sign-in with Google capability, providing Apple users with a streamlined way to access AWS resources without managing separate credentials on AWS.
• AWS STS now supports validation of select identity provider specific claims from Google, GitHub, CircleCI and OCI: You can reference these custom claims as condition keys in IAM role trust policies and resource control policies, expanding your ability to implement fine-grained access control for federated identities and help you establish your data perimeters. This enhancement builds upon IAM’s existing OIDC federation capabilities, which allow you to grant temporary AWS credentials to users authenticated through external OIDC-compatible identity providers.
• AWS Management Console now displays Account Name on the Navigation bar for easier account identification: You now have an easy way to identify your accounts at a glance. You can now quickly distinguish between accounts visually using the account name that appears in the navigation bar for all authorized users in that account.
• Amazon CloudFront announces mutual TLS support for origins: Now with origin mTLS support, you can implement a standardized, certificate-based authentication approach that eliminates operational burden. This enables organizations to enforce strict authentication for their proprietary content, ensuring that only verified CloudFront distributions can establish connections to backend infrastructure ranging from AWS origins and on-premises servers to third-party cloud providers and external CDNs.

Finally, there is not a single week without news around AI :

• Claude Opus 4.6 now available in Amazon Bedrock: Opus 4.6 is Anthropic’s most intelligent model to date and a premier model for coding, enterprise agents, and professional work. Claude Opus 4.6 brings advanced capabilities to Amazon Bedrock customers, including industry-leading performance for agentic tasks, complex coding projects, and enterprise-grade workflows that require deep reasoning and reliability.
• Structured outputs now available in Amazon Bedrock: Amazon Bedrock now supports structured outputs, a capability that provides consistent, machine-readable responses from foundation models that adhere to your defined JSON schemas. Instead of prompting for valid JSON and adding extra checks in your application, you can specify the format you want and receive responses that match it—making production workflows more predictable and resilient.

Upcoming AWS events

Check your calendars so that you can sign up for this upcoming event:

AWS Community Day Romania (April 23–24, 2026): This community-led AWS event brings together developers, architects, entrepreneurs, and students for more than 10 professional sessions delivered by AWS Heroes, Solutions Architects, and industry experts. Attendees can expect expert-led technical talks, insights from speakers with global conference experience, and opportunities to connect during dedicated networking breaks, all hosted at a premium venue designed to support collaboration and community engagement.

If you’re looking for more ways to stay connected beyond this event, join the AWS Builder Center to learn, build, and connect with builders in the AWS community.

Check back next Monday for another Weekly Roundup.

— seb

The post AWS Weekly Roundup: Claude Opus 4.6 in Amazon Bedrock, AWS Builder ID Sign in with Apple, and more (February 9, 2026) appeared first on The NOKIA Blog.

Last week I met Jeff Barr at the AWS Shenzhen Community Day. Jeff shared stories about how builders around the world are experimenting with generative AI and encouraged local developers to keep pushing ideas into real prototypes. Many attendees stayed after the sessions to discuss model grounding, evaluation, and how to bring generative AI into real applications.

Community builders showcased creative Kiro-themed demos, AI-powered IoT projects, and student-led experiments. It was inspiring to see new developers, students, and long-time Amazon Web Services (AWS) community leaders connecting over shared curiosity and excitement for generative AI innovation.

Project Rainier, one of the world’s most powerful operational AI supercomputers is now online. Built by AWS in close collaboration with Anthropic, Project Rainier brings nearly 500,000 AWS custom-designed Trainium2 chips into service using a new Amazon Elastic Compute (Amazon EC2) UltraServer and EC2 UltraCluster architecture designed for high-bandwidth, low-latency model training at hyperscale.

Anthropic is already training and running inference for Claude on Project Rainier, and is expected to scale to more than one million Trainium2 chips across direct usage and Amazon Bedrock by the end of 2025. For architecture details, deployment insights, and behind-the-scenes video of an UltraServer coming online, refer to AWS activates Project Rainier for the full announcement.

Last week’s launches
Here are the launches that got my attention this week:

• Amazon Nova – Adds Web Grounding as a new built-in tool for real-time, citation-based web retrieval, and introduces Multimodal Embeddings, a state-of-the-art model that produces unified cross-modal vectors, improving accuracy for Retrieval Augmented Generation (RAG) and semantic search. Both capabilities are available in Amazon Bedrock.
• Amazon Bedrock – TwelveLabs’ Marengo Embed 3.0 is now available for long-form, video-native multimodal embeddings across video, images, audio, and text with improved domain accuracy. Stability AI Image Services added four new tools: Outpaint, Fast Upscale, Conservative Upscale, and Creative Upscale for high-resolution upscaling, outpainting, and controlled variations.
• Model Context Protocol (MCP) Proxy for AWS – Now generally available as a client-side proxy that connects MCP clients to remote AWS hosted MCP servers using SigV4 authentication. It works with tools like Amazon Q Developer CLI, Kiro, Cursor, and Strands Agents, and provides safety controls such as read-only mode, retry logic, and logging. The Proxy is open-source. You can visit the AWS GitHub repository to view the installation and configuration options and start connecting with remote AWS MCP servers.
• Amazon Elastic Container Service (Amazon ECS) – Now supports built-in linear and canary deployment strategies, providing gradual traffic shifting, canary testing with small production slices, deployment bake times for safe rollback, and Amazon CloudWatch alarm-based automated rollbacks.
• Amazon DocumentDB – Adds a new query planner in Amazon DocumentDB 5.0 that delivers up to 10 times faster query performance with more optimal index plans and support for $neq, $nin, and nested $elementMatch, and can be enabled through cluster parameter groups without downtime.
• Amazon Elastic Block Store (Amazon EBS) – You can now use new per-volume CloudWatch metrics, VolumeAvgIOPS and VolumeAvgThroughput, to get minute-level visibility into average IOPS and throughput for EBS volumes on AWS Nitro based instances. These metrics help monitor performance trends, troubleshoot bottlenecks, and optimize provisioned capacity.
• Amazon Kinesis Data Streams – You can now send individual records up to 10 MiB, a tenfold increase from the previous limit, helping support larger Internet of Things (IoT), change data capture (CDC), and AI-generated payloads.
  Amazon SageMaker – Unified Studio search results now provide additional search context, showing matched metadata fields and ranking rationale to improve transparency and relevance in data discovery.

Additional updates
Here are some additional projects, blog posts, and news items that I found interesting:

• Building production-ready 3D pipelines with AWS VAMS and 4D Pipeline – A reference architecture for creating scalable, cloud-based 3D asset pipelines using AWS Visual Asset Management System (VAMS) and 4D Pipeline, supporting ingest, validation, collaborative review, and distribution across games, visual effects (VFX), and digital twins.
• Amazon Location Service introduces new API key restrictions – You can now create granular security policies with bundle IDs to restrict API access to specific mobile applications, improving access control and strengthening application-level security across location-based workloads.
• AWS Clean Rooms launches advanced SQL configurations – A performance enhancement for Spark SQL workloads that supports runtime customization of Spark properties and compute sizes, plus table caching for faster and more cost-efficient processing of large analytical queries.
• AWS Serverless MCP Server adds event source mappings (ESM) tools – A capability for event-driven serverless applications that supports configuration, performance tuning, and troubleshooting of AWS Lambda event source mappings, including AWS Serverless Application Model (AWS SAM) template generation and diagnostic insights.
• AWS IoT Greengrass releases an AI agent context pack – A development accelerator for cloud-connected edge applications that provides ready-to-use instructions, examples, and templates, helping teams integrate generative AI tools such as Amazon Q for faster software creation, testing, and fleet-wide deployment. It’s available as open source on the GitHub repository.
• AWS Step Functions introduces a new metrics dashboard – You can now view usage, billing, and performance metrics at the state-machine level for standard and express workflows in a single console view, improving visibility and troubleshooting for distributed applications.

Upcoming AWS events
Check your calendars so that you can sign up for these upcoming events:

• AWS Builder Loft – A community tech space in San Francisco where you can learn from expert sessions, join hands-on workshops, explore AI and emerging technologies, and collaborate with other builders to accelerate their ideas. Browse the upcoming sessions and join the events that interest you.
• AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by experienced AWS users and industry leaders from around the world: Hong Kong (November 2), Abuja (November 8), Cameroon (November 8), and Spain (November 15).
• AWS Skills Center Seattle 4th Anniversary Celebration – A free, public event on November 20 with a keynote, learned panels, recruiter insights, raffles, and virtual participation options.

Join the AWS Builder Center to learn, build, and connect with builders in the AWS community. Browse here for upcoming in-person events, developer-focused events, and events for startups.

That’s all for this week. Check back next Monday for another Weekly Roundup!

– Betty

The post AWS Weekly Roundup: Project Rainier online, Amazon Nova, Amazon Bedrock, and more (November 3, 2025) appeared first on The NOKIA Blog.

Today, we’re introducing Amazon Nova Multimodal Embeddings, a state-of-the-art multimodal embedding model for agentic retrieval-augmented generation (RAG) and semantic search applications, available in Amazon Bedrock. It is the first unified embedding model that supports text, documents, images, video, and audio through a single model to enable crossmodal retrieval with leading accuracy.

Embedding models convert textual, visual, and audio inputs into numerical representations called embeddings. These embeddings capture the meaning of the input in a way that AI systems can compare, search, and analyze, powering use cases such as semantic search and RAG.

Organizations are increasingly seeking solutions to unlock insights from the growing volume of unstructured data that is spread across text, image, document, video, and audio content. For example, an organization might have product images, brochures that contain infographics and text, and user-uploaded video clips. Embedding models are able to unlock value from unstructured data, however traditional models are typically specialized to handle one content type. This limitation drives customers to either build complex crossmodal embedding solutions or restrict themselves to use cases focused on a single content type. The problem also applies to mixed-modality content types such as documents with interleaved text and images or video with visual, audio, and textual elements where existing models struggle to capture crossmodal relationships effectively.

Nova Multimodal Embeddings supports a unified semantic space for text, documents, images, video, and audio for use cases such as crossmodal search across mixed-modality content, searching with a reference image, and retrieving visual documents.

Evaluating Amazon Nova Multimodal Embeddings performance
We evaluated the model on a broad range of benchmarks, and it delivers leading accuracy out-of-the-box as described in the following table.

Nova Multimodal Embeddings supports a context length of up to 8K tokens, text in up to 200 languages, and accepts inputs via synchronous and asynchronous APIs. Additionally, it supports segmentation (also known as “chunking”) to partition long-form text, video, or audio content into manageable segments, generating embeddings for each portion. Lastly, the model offers four output embedding dimensions, trained using Matryoshka Representation Learning (MRL) that enables low-latency end-to-end retrieval with minimal accuracy changes.

Let’s see how the new model can be used in practice.

Using Amazon Nova Multimodal Embeddings
Getting started with Nova Multimodal Embeddings follows the same pattern as other models in Amazon Bedrock. The model accepts text, documents, images, video, or audio as input and returns numerical embeddings that you can use for semantic search, similarity comparison, or RAG.

Here’s a practical example using the AWS SDK for Python (Boto3) that shows how to create embeddings from different content types and store them for later retrieval. For simplicity, I’ll use Amazon S3 Vectors, a cost-optimized storage with native support for storing and querying vectors at any scale, to store and search the embeddings.

Let’s start with the fundamentals: converting text into embeddings. This example shows how to transform a simple text description into a numerical representation that captures its semantic meaning. These embeddings can later be compared with embeddings from documents, images, videos, or audio to find related content.

To make the code easy to follow, I’ll show a section of the script at a time. The full script is included at the end of this walkthrough.

import json
import base64
import time
import boto3

MODEL_ID = "amazon.nova-2-multimodal-embeddings-v1:0"
EMBEDDING_DIMENSION = 3072

# Initialize Amazon Bedrock Runtime client
bedrock_runtime = boto3.client("bedrock-runtime", region_name="us-east-1")

print(f"Generating text embedding with {MODEL_ID} ...")

# Text to embed
text = "Amazon Nova is a multimodal foundation model"

# Create embedding
request_body = {
    "taskType": "SINGLE_EMBEDDING",
    "singleEmbeddingParams": {
        "embeddingPurpose": "GENERIC_INDEX",
        "embeddingDimension": EMBEDDING_DIMENSION,
        "text": {"truncationMode": "END", "value": text},
    },
}

response = bedrock_runtime.invoke_model(
    body=json.dumps(request_body),
    modelId=MODEL_ID,
    contentType="application/json",
)

# Extract embedding
response_body = json.loads(response["body"].read())
embedding = response_body["embeddings"][0]["embedding"]

print(f"Generated embedding with {len(embedding)} dimensions")

Now we’ll process visual content using the same embedding space using a photo.jpg file in the same folder as the script. This demonstrates the power of multimodality: Nova Multimodal Embeddings is able to capture both textual and visual context into a single embedding that provides enhanced understanding of the document.

Nova Multimodal Embeddings can generate embeddings that are optimized for how they are being used. When indexing for a search or retrieval use case, embeddingPurpose can be set to GENERIC_INDEX. For the query step, embeddingPurpose can be set depending on the type of item to be retrieved. For example, when retrieving documents, embeddingPurpose can be set to DOCUMENT_RETRIEVAL.

# Read and encode image
print(f"Generating image embedding with {MODEL_ID} ...")

with open("photo.jpg", "rb") as f:
    image_bytes = base64.b64encode(f.read()).decode("utf-8")

# Create embedding
request_body = {
    "taskType": "SINGLE_EMBEDDING",
    "singleEmbeddingParams": {
        "embeddingPurpose": "GENERIC_INDEX",
        "embeddingDimension": EMBEDDING_DIMENSION,
        "image": {
            "format": "jpeg",
            "source": {"bytes": image_bytes}
        },
    },
}

response = bedrock_runtime.invoke_model(
    body=json.dumps(request_body),
    modelId=MODEL_ID,
    contentType="application/json",
)

# Extract embedding
response_body = json.loads(response["body"].read())
embedding = response_body["embeddings"][0]["embedding"]

print(f"Generated embedding with {len(embedding)} dimensions")

To process video content, I use the asynchronous API. That’s a requirement for videos that are larger than 25MB when encoded as Base64. First, I upload a local video to an S3 bucket in the same AWS Region.

aws s3 cp presentation.mp4 s3://my-video-bucket/videos/

This example shows how to extract embeddings from both visual and audio components of a video file. The segmentation feature breaks longer videos into manageable chunks, making it practical to search through hours of content efficiently.

# Initialize Amazon S3 client
s3 = boto3.client("s3", region_name="us-east-1")

print(f"Generating video embedding with {MODEL_ID} ...")

# Amazon S3 URIs
S3_VIDEO_URI = "s3://my-video-bucket/videos/presentation.mp4"
S3_EMBEDDING_DESTINATION_URI = "s3://my-embedding-destination-bucket/embeddings-output/"

# Create async embedding job for video with audio
model_input = {
    "taskType": "SEGMENTED_EMBEDDING",
    "segmentedEmbeddingParams": {
        "embeddingPurpose": "GENERIC_INDEX",
        "embeddingDimension": EMBEDDING_DIMENSION,
        "video": {
            "format": "mp4",
            "embeddingMode": "AUDIO_VIDEO_COMBINED",
            "source": {
                "s3Location": {"uri": S3_VIDEO_URI}
            },
            "segmentationConfig": {
                "durationSeconds": 15  # Segment into 15-second chunks
            },
        },
    },
}

response = bedrock_runtime.start_async_invoke(
    modelId=MODEL_ID,
    modelInput=model_input,
    outputDataConfig={
        "s3OutputDataConfig": {
            "s3Uri": S3_EMBEDDING_DESTINATION_URI
        }
    },
)

invocation_arn = response["invocationArn"]
print(f"Async job started: {invocation_arn}")

# Poll until job completes
print("nPolling for job completion...")
while True:
    job = bedrock_runtime.get_async_invoke(invocationArn=invocation_arn)
    status = job["status"]
    print(f"Status: {status}")

    if status != "InProgress":
        break
    time.sleep(15)

# Check if job completed successfully
if status == "Completed":
    output_s3_uri = job["outputDataConfig"]["s3OutputDataConfig"]["s3Uri"]
    print(f"nSuccess! Embeddings at: {output_s3_uri}")

    # Parse S3 URI to get bucket and prefix
    s3_uri_parts = output_s3_uri[5:].split("/", 1)  # Remove "s3://" prefix
    bucket = s3_uri_parts[0]
    prefix = s3_uri_parts[1] if len(s3_uri_parts) > 1 else ""

    # AUDIO_VIDEO_COMBINED mode outputs to embedding-audio-video.jsonl
    # The output_s3_uri already includes the job ID, so just append the filename
    embeddings_key = f"{prefix}/embedding-audio-video.jsonl".lstrip("/")

    print(f"Reading embeddings from: s3://{bucket}/{embeddings_key}")

    # Read and parse JSONL file
    response = s3.get_object(Bucket=bucket, Key=embeddings_key)
    content = response['Body'].read().decode('utf-8')

    embeddings = []
    for line in content.strip().split('n'):
        if line:
            embeddings.append(json.loads(line))

    print(f"nFound {len(embeddings)} video segments:")
    for i, segment in enumerate(embeddings):
        print(f"  Segment {i}: {segment.get('startTime', 0):.1f}s - {segment.get('endTime', 0):.1f}s")
        print(f"    Embedding dimension: {len(segment.get('embedding', []))}")
else:
    print(f"nJob failed: {job.get('failureMessage', 'Unknown error')}")

With our embeddings generated, we need a place to store and search them efficiently. This example demonstrates setting up a vector store using Amazon S3 Vectors, which provides the infrastructure needed for similarity search at scale. Think of this as creating a searchable index where semantically similar content naturally clusters together. When adding an embedding to the index, I use the metadata to specify the original format and the content being indexed.

# Initialize Amazon S3 Vectors client
s3vectors = boto3.client("s3vectors", region_name="us-east-1")

# Configuration
VECTOR_BUCKET = "my-vector-store"
INDEX_NAME = "embeddings"

# Create vector bucket and index (if they don't exist)
try:
    s3vectors.get_vector_bucket(vectorBucketName=VECTOR_BUCKET)
    print(f"Vector bucket {VECTOR_BUCKET} already exists")
except s3vectors.exceptions.NotFoundException:
    s3vectors.create_vector_bucket(vectorBucketName=VECTOR_BUCKET)
    print(f"Created vector bucket: {VECTOR_BUCKET}")

try:
    s3vectors.get_index(vectorBucketName=VECTOR_BUCKET, indexName=INDEX_NAME)
    print(f"Vector index {INDEX_NAME} already exists")
except s3vectors.exceptions.NotFoundException:
    s3vectors.create_index(
        vectorBucketName=VECTOR_BUCKET,
        indexName=INDEX_NAME,
        dimension=EMBEDDING_DIMENSION,
        dataType="float32",
        distanceMetric="cosine"
    )
    print(f"Created index: {INDEX_NAME}")

texts = [
    "Machine learning on AWS",
    "Amazon Bedrock provides foundation models",
    "S3 Vectors enables semantic search"
]

print(f"nGenerating embeddings for {len(texts)} texts...")

# Generate embeddings using Amazon Nova for each text
vectors = []
for text in texts:
    response = bedrock_runtime.invoke_model(
        body=json.dumps({
            "taskType": "SINGLE_EMBEDDING",
            "singleEmbeddingParams": {
                "embeddingDimension": EMBEDDING_DIMENSION,
                "text": {"truncationMode": "END", "value": text}
            }
        }),
        modelId=MODEL_ID,
        accept="application/json",
        contentType="application/json"
    )

    response_body = json.loads(response["body"].read())
    embedding = response_body["embeddings"][0]["embedding"]

    vectors.append({
        "key": f"text:{text[:50]}",  # Unique identifier
        "data": {"float32": embedding},
        "metadata": {"type": "text", "content": text}
    })
    print(f"  ✓ Generated embedding for: {text}")

# Add all vectors to store in a single call
s3vectors.put_vectors(
    vectorBucketName=VECTOR_BUCKET,
    indexName=INDEX_NAME,
    vectors=vectors
)

print(f"nSuccessfully added {len(vectors)} vectors to the store in one put_vectors call!")

This final example demonstrates the capability of searching across different content types with a single query, finding the most similar content regardless of whether it originated from text, images, videos, or audio. The distance scores help you understand how closely related the results are to your original query.

# Text to query
query_text = "foundation models"  

print(f"nGenerating embeddings for query '{query_text}' ...")

# Generate embeddings
response = bedrock_runtime.invoke_model(
    body=json.dumps({
        "taskType": "SINGLE_EMBEDDING",
        "singleEmbeddingParams": {
            "embeddingPurpose": "GENERIC_RETRIEVAL",
            "embeddingDimension": EMBEDDING_DIMENSION,
            "text": {"truncationMode": "END", "value": query_text}
        }
    }),
    modelId=MODEL_ID,
    accept="application/json",
    contentType="application/json"
)

response_body = json.loads(response["body"].read())
query_embedding = response_body["embeddings"][0]["embedding"]

print(f"Searching for similar embeddings...n")

# Search for top 5 most similar vectors
response = s3vectors.query_vectors(
    vectorBucketName=VECTOR_BUCKET,
    indexName=INDEX_NAME,
    queryVector={"float32": query_embedding},
    topK=5,
    returnDistance=True,
    returnMetadata=True
)

# Display results
print(f"Found {len(response['vectors'])} results:n")
for i, result in enumerate(response["vectors"], 1):
    print(f"{i}. {result['key']}")
    print(f"   Distance: {result['distance']:.4f}")
    if result.get("metadata"):
        print(f"   Metadata: {result['metadata']}")
    print()

Crossmodal search is one of the key advantages of multimodal embeddings. With crossmodal search, you can query with text and find relevant images. You can also search for videos using text descriptions, find audio clips that match certain topics, or discover documents based on their visual and textual content. For your reference, the full script with all previous examples merged together is here:

import json
import base64
import time
import boto3

MODEL_ID = "amazon.nova-2-multimodal-embeddings-v1:0"
EMBEDDING_DIMENSION = 3072

# Initialize Amazon Bedrock Runtime client
bedrock_runtime = boto3.client("bedrock-runtime", region_name="us-east-1")

print(f"Generating text embedding with {MODEL_ID} ...")

# Text to embed
text = "Amazon Nova is a multimodal foundation model"

# Create embedding
request_body = {
    "taskType": "SINGLE_EMBEDDING",
    "singleEmbeddingParams": {
        "embeddingPurpose": "GENERIC_INDEX",
        "embeddingDimension": EMBEDDING_DIMENSION,
        "text": {"truncationMode": "END", "value": text},
    },
}

response = bedrock_runtime.invoke_model(
    body=json.dumps(request_body),
    modelId=MODEL_ID,
    contentType="application/json",
)

# Extract embedding
response_body = json.loads(response["body"].read())
embedding = response_body["embeddings"][0]["embedding"]

print(f"Generated embedding with {len(embedding)} dimensions")
# Read and encode image
print(f"Generating image embedding with {MODEL_ID} ...")

with open("photo.jpg", "rb") as f:
    image_bytes = base64.b64encode(f.read()).decode("utf-8")

# Create embedding
request_body = {
    "taskType": "SINGLE_EMBEDDING",
    "singleEmbeddingParams": {
        "embeddingPurpose": "GENERIC_INDEX",
        "embeddingDimension": EMBEDDING_DIMENSION,
        "image": {
            "format": "jpeg",
            "source": {"bytes": image_bytes}
        },
    },
}

response = bedrock_runtime.invoke_model(
    body=json.dumps(request_body),
    modelId=MODEL_ID,
    contentType="application/json",
)

# Extract embedding
response_body = json.loads(response["body"].read())
embedding = response_body["embeddings"][0]["embedding"]

print(f"Generated embedding with {len(embedding)} dimensions")
# Initialize Amazon S3 client
s3 = boto3.client("s3", region_name="us-east-1")

print(f"Generating video embedding with {MODEL_ID} ...")

# Amazon S3 URIs
S3_VIDEO_URI = "s3://my-video-bucket/videos/presentation.mp4"

# Amazon S3 output bucket and location
S3_EMBEDDING_DESTINATION_URI = "s3://my-video-bucket/embeddings-output/"

# Create async embedding job for video with audio
model_input = {
    "taskType": "SEGMENTED_EMBEDDING",
    "segmentedEmbeddingParams": {
        "embeddingPurpose": "GENERIC_INDEX",
        "embeddingDimension": EMBEDDING_DIMENSION,
        "video": {
            "format": "mp4",
            "embeddingMode": "AUDIO_VIDEO_COMBINED",
            "source": {
                "s3Location": {"uri": S3_VIDEO_URI}
            },
            "segmentationConfig": {
                "durationSeconds": 15  # Segment into 15-second chunks
            },
        },
    },
}

response = bedrock_runtime.start_async_invoke(
    modelId=MODEL_ID,
    modelInput=model_input,
    outputDataConfig={
        "s3OutputDataConfig": {
            "s3Uri": S3_EMBEDDING_DESTINATION_URI
        }
    },
)

invocation_arn = response["invocationArn"]
print(f"Async job started: {invocation_arn}")

# Poll until job completes
print("nPolling for job completion...")
while True:
    job = bedrock_runtime.get_async_invoke(invocationArn=invocation_arn)
    status = job["status"]
    print(f"Status: {status}")

    if status != "InProgress":
        break
    time.sleep(15)

# Check if job completed successfully
if status == "Completed":
    output_s3_uri = job["outputDataConfig"]["s3OutputDataConfig"]["s3Uri"]
    print(f"nSuccess! Embeddings at: {output_s3_uri}")

    # Parse S3 URI to get bucket and prefix
    s3_uri_parts = output_s3_uri[5:].split("/", 1)  # Remove "s3://" prefix
    bucket = s3_uri_parts[0]
    prefix = s3_uri_parts[1] if len(s3_uri_parts) > 1 else ""

    # AUDIO_VIDEO_COMBINED mode outputs to embedding-audio-video.jsonl
    # The output_s3_uri already includes the job ID, so just append the filename
    embeddings_key = f"{prefix}/embedding-audio-video.jsonl".lstrip("/")

    print(f"Reading embeddings from: s3://{bucket}/{embeddings_key}")

    # Read and parse JSONL file
    response = s3.get_object(Bucket=bucket, Key=embeddings_key)
    content = response['Body'].read().decode('utf-8')

    embeddings = []
    for line in content.strip().split('n'):
        if line:
            embeddings.append(json.loads(line))

    print(f"nFound {len(embeddings)} video segments:")
    for i, segment in enumerate(embeddings):
        print(f"  Segment {i}: {segment.get('startTime', 0):.1f}s - {segment.get('endTime', 0):.1f}s")
        print(f"    Embedding dimension: {len(segment.get('embedding', []))}")
else:
    print(f"nJob failed: {job.get('failureMessage', 'Unknown error')}")
# Initialize Amazon S3 Vectors client
s3vectors = boto3.client("s3vectors", region_name="us-east-1")

# Configuration
VECTOR_BUCKET = "my-vector-store"
INDEX_NAME = "embeddings"

# Create vector bucket and index (if they don't exist)
try:
    s3vectors.get_vector_bucket(vectorBucketName=VECTOR_BUCKET)
    print(f"Vector bucket {VECTOR_BUCKET} already exists")
except s3vectors.exceptions.NotFoundException:
    s3vectors.create_vector_bucket(vectorBucketName=VECTOR_BUCKET)
    print(f"Created vector bucket: {VECTOR_BUCKET}")

try:
    s3vectors.get_index(vectorBucketName=VECTOR_BUCKET, indexName=INDEX_NAME)
    print(f"Vector index {INDEX_NAME} already exists")
except s3vectors.exceptions.NotFoundException:
    s3vectors.create_index(
        vectorBucketName=VECTOR_BUCKET,
        indexName=INDEX_NAME,
        dimension=EMBEDDING_DIMENSION,
        dataType="float32",
        distanceMetric="cosine"
    )
    print(f"Created index: {INDEX_NAME}")

texts = [
    "Machine learning on AWS",
    "Amazon Bedrock provides foundation models",
    "S3 Vectors enables semantic search"
]

print(f"nGenerating embeddings for {len(texts)} texts...")

# Generate embeddings using Amazon Nova for each text
vectors = []
for text in texts:
    response = bedrock_runtime.invoke_model(
        body=json.dumps({
            "taskType": "SINGLE_EMBEDDING",
            "singleEmbeddingParams": {
                "embeddingPurpose": "GENERIC_INDEX",
                "embeddingDimension": EMBEDDING_DIMENSION,
                "text": {"truncationMode": "END", "value": text}
            }
        }),
        modelId=MODEL_ID,
        accept="application/json",
        contentType="application/json"
    )

    response_body = json.loads(response["body"].read())
    embedding = response_body["embeddings"][0]["embedding"]

    vectors.append({
        "key": f"text:{text[:50]}",  # Unique identifier
        "data": {"float32": embedding},
        "metadata": {"type": "text", "content": text}
    })
    print(f"  ✓ Generated embedding for: {text}")

# Add all vectors to store in a single call
s3vectors.put_vectors(
    vectorBucketName=VECTOR_BUCKET,
    indexName=INDEX_NAME,
    vectors=vectors
)

print(f"nSuccessfully added {len(vectors)} vectors to the store in one put_vectors call!")
# Text to query
query_text = "foundation models"  

print(f"nGenerating embeddings for query '{query_text}' ...")

# Generate embeddings
response = bedrock_runtime.invoke_model(
    body=json.dumps({
        "taskType": "SINGLE_EMBEDDING",
        "singleEmbeddingParams": {
            "embeddingPurpose": "GENERIC_RETRIEVAL",
            "embeddingDimension": EMBEDDING_DIMENSION,
            "text": {"truncationMode": "END", "value": query_text}
        }
    }),
    modelId=MODEL_ID,
    accept="application/json",
    contentType="application/json"
)

response_body = json.loads(response["body"].read())
query_embedding = response_body["embeddings"][0]["embedding"]

print(f"Searching for similar embeddings...n")

# Search for top 5 most similar vectors
response = s3vectors.query_vectors(
    vectorBucketName=VECTOR_BUCKET,
    indexName=INDEX_NAME,
    queryVector={"float32": query_embedding},
    topK=5,
    returnDistance=True,
    returnMetadata=True
)

# Display results
print(f"Found {len(response['vectors'])} results:n")
for i, result in enumerate(response["vectors"], 1):
    print(f"{i}. {result['key']}")
    print(f"   Distance: {result['distance']:.4f}")
    if result.get("metadata"):
        print(f"   Metadata: {result['metadata']}")
    print()

For production applications, embeddings can be stored in any vector database. Amazon OpenSearch Service offers native integration with Nova Multimodal Embeddings at launch, making it straightforward to build scalable search applications. As shown in the examples before, Amazon S3 Vectors provides a simple way to store and query embeddings with your application data.

Things to know
Nova Multimodal Embeddings offers four output dimension options: 3,072, 1,024, 384, and 256. Larger dimensions provide more detailed representations but require more storage and computation. Smaller dimensions offer a practical balance between retrieval performance and resource efficiency. This flexibility helps you optimize for your specific application and cost requirements.

The model handles substantial context lengths. For text inputs, it can process up to 8,192 tokens at once. Video and audio inputs support segments of up to 30 seconds, and the model can segment longer files. This segmentation capability is particularly useful when working with large media files—the model splits them into manageable pieces and creates embeddings for each segment.

The model includes responsible AI features built into Amazon Bedrock. Content submitted for embedding goes through Amazon Bedrock content safety filters, and the model includes fairness measures to reduce bias.

As described in the code examples, the model can be invoked through both synchronous and asynchronous APIs. The synchronous API works well for real-time applications where you need immediate responses, such as processing user queries in a search interface. The asynchronous API handles latency insensitive workloads more efficiently, making it suitable for processing large content such as videos.

Availability and pricing
Amazon Nova Multimodal Embeddings is available today in Amazon Bedrock in the US East (N. Virginia) AWS Region. For detailed pricing information, visit the Amazon Bedrock pricing page.

To learn more, see the Amazon Nova User Guide for comprehensive documentation and the Amazon Nova model cookbook on GitHub for practical code examples.

If you’re using an AI–powered assistant for software development such as Amazon Q Developer or Kiro, you can set up the AWS API MCP Server to help the AI assistants interact with AWS services and resources and the AWS Knowledge MCP Server to provide up-to-date documentation, code samples, knowledge about the regional availability of AWS APIs and CloudFormation resources.

Start building multimodal AI-powered applications with Nova Multimodal Embeddings today, and share your feedback through AWS re:Post for Amazon Bedrock or your usual AWS Support contacts.

— Danilo

The post Amazon Nova Multimodal Embeddings: State-of-the-art embedding model for agentic RAG and semantic search appeared first on The NOKIA Blog.

I’ve been inspired by all the activities that tech communities around the world have been hosting and participating in throughout the year. Here in the southern hemisphere we’re starting to dream about our upcoming summer breaks and closing out on some of the activities we’ve initiated this year. The tech community in South Africa is participating in Amazon Q Developer coding challenges that my colleagues and I are hosting throughout this month as a fun way to wind down activities for the year. The first one was hosted in Johannesburg last Friday with Durban and Cape Town coming up next.

Last week’s launches
These are the launches from last week that caught my attention:

• Kiro is now available for every developer — Since its launch more than 90 days ago, more than 100,000 developers have joined the waitlist to try Kiro out. The waitlist is gone so if you want to try out this spec-driven approach to coding with AI, sign up now.
• Amazon EC2 Capacity Manager — If you’re using Amazon Elastic Compute Cloud (Amazon EC2) at scale operate hundreds of instance types across multiple Availability Zones and accounts, using On-Demand Instances, Spot Instances, and Capacity Reservations, you’ll be pleased to learn that EC2 Capacity Manager is now available to provide you a centralized solution to monitor, analyze, and manage capacity usage across all account and AWS Regions from a single interface.
• Amazon EBS Volume Clones — Sometimes you need production data to test a fix in a non-production environment before implementing it in production. Usually you’d take an EBS snapshot of this data and then create a new volume from that snapshot, meanwhile dealing with the operational overhead of this process. Learn about the availability of Amazon EBS Volume Clones, a new capability for you to create instant point-in-time copies of your EBS volumes within the same Availability Zone.

Additional updates
I thought these projects, blog posts, and news items were also interesting:

Upcoming AWS events
Keep a look out and be sure to sign up for these upcoming events:

AWS re:Invent 2025 (December 1-5, 2025, Las Vegas) — AWS flagship annual conference offering collaborative innovation through peer-to-peer learning, expert-led discussions, and invaluable networking opportunities.

Join the AWS Builder Center to learn, build, and connect with builders in the AWS community. Browse here for upcoming in-person and virtual developer-focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

– Veliswa.

The post AWS Weekly Roundup: Kiro waitlist, EBS Volume Clones, EC2 Capacity Manager, and more (October 20, 2025) appeared first on The NOKIA Blog.

Starting today, you can use your own AWS Key Management Service (AWS KMS) keys to encrypt identity data, such as user and group attributes, stored in AWS IAM Identity Center organization instances.

Many organizations operating in regulated industries need complete control over encryption key management. While Identity Center already encrypts data at rest using AWS-owned keys, some customers require the ability to manage their own encryption keys for audit and compliance purposes.

With this launch, you can now use customer-managed KMS keys (CMKs) to encrypt Identity Center identity data at rest. CMKs provide you with full control over the key lifecycle, including creation, rotation, and deletion. You can configure granular access controls to keys with AWS Key Management Service (AWS KMS) key policies and IAM policies, helping to ensure that only authorized principals can access your encrypted data. At launch time, the CMK must reside in the same AWS account and Region as your IAM Identity Center instance. The integration between Identity Center and KMS provides detailed AWS CloudTrail logs for auditing key usage and helps meet regulatory compliance requirements.

Identity Center supports both single-Region and multi-Region keys to match your deployment needs. While Identity Center instances can currently only be deployed in a single Region, we recommend using multi-Region AWS KMS keys unless your company policies restrict you to single-Region keys. Multi-Region keys provide consistent key material across Regions while maintaining independent key infrastructure in each Region. This gives you more flexibility in your encryption strategy and helps future-proof your deployment.

Let’s get started
Let’s imagine I want to use a CMK to encrypt the identity data of my Identity Center organization instance. My organization uses Identity Center to give employees access to AWS managed applications, such as Amazon Q Business or Amazon Athena.

As of today, some AWS managed applications cannot be used with Identity Center configured with a customer managed KMS key. See AWS managed applications that you can use with Identity Center to keep you updated with the ever evolving list of compatible applications.

The high-level process requires first to create a symmetric customer managed key (CMK) in AWS KMS. The key must be configured for encrypt and decrypt operations. Next, I configure the key policies to grant access to Identity Center, AWS managed applications, administrators, and other principals who need access the Identity Center and IAM Identity Center service APIs. Depending on your usage of Identity Center, you’ll have to define different policies for the key and IAM policies for IAM principals. The service documentation has more details to help you cover the most common use cases.

This demo is in three parts. I first create a customer managed key in AWS KMS and configure it with permissions that will authorize Identity Center and AWS managed applications to use it. Second, I update the IAM policies for the principals that will use the key from another AWS account, such as AWS applications administrators. Finally, I configure Identity Center to use the key.

Part 1: Create the key and define permissions

First, let’s create a new CMK in AWS KMS.

The key must be in the same AWS Region and AWS account as the Identity Center instance. You must create the Identity Center instance and the key in the management account of your organization within AWS Organization.

I navigate to the AWS Key Management Service (AWS KMS) console in the same Region as my Identity Center instance, then I choose Create a key. This launches me into the key creation wizard.

Under Step 1–Configure key, I select the key type–either Symmetric (a single key used for both encryption and decryption) or Asymmetric (a public-private key pair for encryption/decryption and signing/verification). Identity Center requires symmetric keys for encryption at rest. I select Symmetric.

For key usage, I select Encrypt and decrypt which allows the key to be used only for encrypting and decrypting data.

Under Advanced options, I select KMS – recommended for Key material origin, so AWS KMS creates and manages the key material.

For Regionality, I choose between Single-Region or Multi-Region key. I select Multi-Region key to allow key administrators to replicate the key to other Regions. As explained already, Identity Center doesn’t require this today but it helps to future-proof your configuration. Remember that you can not transform a single-Region key to a multi-Region one after its creation (but you can change the key used by Identity Center).

Then, I choose Next to proceed with additional configuration steps, such as adding labels, defining administrative permissions, setting usage permissions, and reviewing the final configuration before creating the key.

Under Step 2–Add Labels, I enter an Alias name for my key and select Next.

In this demo, I am editing the key policy by adding policy statements using templates provided in the documentation. I skip Step 3 and Step 4 and navigate to Step 5–Edit key policy.

Identity Center requires, at the minimum, permissions allowing Identity Center and its administrators to use the key. Therefore, I add three policy statements, the first and second authorize the administrators of the service, the third one to authorize the Identity Center service itself.

{
	"Version": "2012-10-17",
	"Id": "key-consolepolicy-3",
	"Statement": [
		{
			"Sid": "Allow_IAMIdentityCenter_Admin_to_use_the_KMS_key_via_IdentityCenter_and_IdentityStore",
			"Effect": "Allow",
			"Principal": {
				"AWS": "ARN_OF_YOUR_IDENTITY_CENTER_ADMIN_IAM_ROLE"
			},
			"Action": [
				"kms:Decrypt",
				"kms:Encrypt",
				"kms:GenerateDataKeyWithoutPlaintext"
			],
			"Resource": "*",
			"Condition": {
				"StringLike": {
					"kms:ViaService": [
						"sso.*.amazonaws.com",
						"identitystore.*.amazonaws.com"
					]
				}
			}
		},
		{
			"Sid": "Allow_IdentityCenter_admin_to_describe_the_KMS_key",
			"Effect": "Allow",
			"Principal": {
				"AWS": "ARN_OF_YOUR_IDENTITY_CENTER_ADMIN_IAM_ROLE"
			},
			"Action": "kms:DescribeKey",
			"Resource": "*"
		},
		{
			"Sid": "Allow_IdentityCenter_and_IdentityStore_to_use_the_KMS_key",
			"Effect": "Allow",
			"Principal": {
				"Service": [
					"sso.amazonaws.com",
					"identitystore.amazonaws.com"
				]
			},
			"Action": [
				"kms:Decrypt",
				"kms:ReEncryptTo",
				"kms:ReEncryptFrom",
				"kms:GenerateDataKeyWithoutPlaintext"
			],
			"Resource": "*",
            "Condition": {
    	       "StringEquals": { 
                      "aws:SourceAccount": "<Identity Center Account ID>" 
	           }
            }		
		},
		{
			"Sid": "Allow_IdentityCenter_and_IdentityStore_to_describe_the_KMS_key",
			"Effect": "Allow",
			"Principal": {
				"Service": [
					"sso.amazonaws.com",
					"identitystore.amazonaws.com"
				]
			},
			"Action": [
				"kms:DescribeKey"
			],
			"Resource": "*"
		}		
	]
}

I also have to add additional policy statements to allow my use case: the use of AWS managed applications. I add these two policy statements to authorize AWS managed applications and their administrators to use the KMS key. The document lists additional use cases and their respective policies.

{
    "Sid": "Allow_AWS_app_admins_in_the_same_AWS_organization_to_use_the_KMS_key",
    "Effect": "Allow",
    "Principal": "*",
    "Action": [
        "kms:Decrypt"
    ],
    "Resource": "*",
    "Condition": {
        "StringEquals" : {
           "aws:PrincipalOrgID": "MY_ORG_ID (format: o-xxxxxxxx)"
        },
        "StringLike": {
            "kms:ViaService": [
                "sso.*.amazonaws.com", "identitystore.*.amazonaws.com"
            ]
        }
    }
},
{
   "Sid": "Allow_managed_apps_to_use_the_KMS_Key",
   "Effect": "Allow",
   "Principal": "*",
   "Action": [
      "kms:Decrypt"
    ],
   "Resource": "*",
   "Condition": {
      "Bool": { "aws:PrincipalIsAWSService": "true" },
      "StringLike": {
         "kms:ViaService": [
             "sso.*.amazonaws.com", "identitystore.*.amazonaws.com"
         ]
      },
      "StringEquals": { "aws:SourceOrgID": "MY_ORG_ID (format: o-xxxxxxxx)" }
   }
}

You can further restrict the key usage to a specific Identity Center instance, specific application instances, or specific application administrators. The documentation contains examples of advanced key policies for your use cases.

To help protect against IAM role name changes when permission sets are recreated, use the approach described in the Custom trust policy example.

Part 2: Update IAM policies to allow use of the KMS key from another AWS account

Any IAM principal that uses the Identity Center service APIs from another AWS account, such as Identity Center delegated administrators and AWS application administrators, need an IAM policy statement that allows use of the KMS key via these APIs.

I grant permissions to access the key by creating a new policy and attaching the policy to the IAM role relevant for my use case. You can also add these statements to the existing identity-based policies of the IAM role.

To do so, after the key is created, I locate its ARN and replace the key_ARNin the template below. Then, I attach the policy to the managed application administrator IAM principal. The documentation also covers IAM policies that grants Identity Center delegated administrators permissions to access the key.

Here is an example for managed application administrators:

{
      "Sid": "Allow_app_admins_to_use_the_KMS_key_via_IdentityCenter_and_IdentityStore",
      "Effect": "Allow",
      "Action": 
        "kms:Decrypt",
      "Resource": "<key_ARN>",
      "Condition": {
        "StringLike": {
          "kms:ViaService": [
            "sso.*.amazonaws.com",
            "identitystore.*.amazonaws.com"
          ]
        }
      }
    }

The documentation shares IAM policies template for the most common use cases.

Part 3: Configure IAM Identity Center to use the key

I can configure a CMK either during the enablement of an Identity Center organization instance or on an existing instance, and I can change the encryption configuration at any time by switching between CMKs or reverting to AWS-owned keys.

Please note that an incorrect configuration of KMS key permissions can disrupt Identity Center operations and access to AWS managed applications and accounts through Identity Center. Proceed carefully to this final step and ensure you have read and understood the documentation.

After I have created and configured my CMK, I can select it under Advanced configuration when enabling Identity Center.

To configure a CMK on an existing Identity Center instance using the AWS Management Console, I start by navigating to the Identity Center section of the AWS Management Console. From there, I select Settings from the navigation pane, then I select the Management tab, and select Manage encryption in the Key for encrypting IAM Identity Center data at rest section.

At any time, I can select another CMK from the same AWS Account, or switch back to an AWS-managed key.

After choosing Save, the key change process takes a few seconds to complete. All service functionalities continue uninterrupted during the transition. If, for whatever reasons, Identity Center can not access the new key, an error message will be returned and Identity Center will continue to use the current key, keeping your identity data encrypted with the mechanism it is already encrypted with.

Things to keep in mind
The encryption key you create becomes a crucial component of your Identity Center. When you choose to use your own managed key to encrypt identity attributes at rest, you have to verify the following points.

• Have you configured the necessary permissions to use the KMS key? Without proper permissions, enabling the CMK may fail or disrupt IAM Identity Center administration and AWS managed applications.
• Have you verified that your AWS managed applications are compatible with CMK keys? For a list of compatible applications, see AWS managed applications that you can use with IAM Identity Center. Enabling CMK for Identity Center that is used by AWS managed applications incompatible with CMK will result in operational disruption for those applications. If you have incompatible applications, do not proceed.
• Is your organization using AWS managed applications that require additional IAM role configuration to use the Identity Center and Identity Store APIs? For each such AWS managed application that’s already deployed, check the managed application’s User Guide for updated KMS key permissions for IAM Identity Centre usage and update them as instructed to prevent application disruption.
• For brevity, the KMS key policy statements in this post omit the encryption context, which allows you to restrict the use of the KMS key to Identity Center including a specific instance. For your production scenarios, you can add a condition like this for Identity Center:

  "Condition": {
     "StringLike": {
        "kms:EncryptionContext:aws:sso:instance-arn": "${identity_center_arn}",
        "kms:ViaService": "sso.*.amazonaws.com"
      }
  }

  or this for Identity Store:

  "Condition": {
     "StringLike": {
        "kms:EncryptionContext:aws:identitystore:identitystore-arn": "${identity_store_arn}",
        "kms:ViaService": "identitystore.*.amazonaws.com"
      }
  }

Pricing and availability
Standard AWS KMS charges apply for key storage and API usage. Identity Center remains available at no additional cost.

This capability is now available in all AWS commercial Regions, AWS GovCloud (US), and AWS China Regions. To learn more, visit the IAM Identity Center User Guide.

We look forward to learning how you use this new capability to meet your security and compliance requirements.

— seb

The post AWS IAM Identity Center now supports customer-managed KMS keys for encryption at rest appeared first on The NOKIA Blog.

Last week, Anthropic’s Claude Sonnet 4.5—the world’s best coding model according to SWE-Bench – became available in Amazon Q command line interface (CLI) and Kiro. I’m excited about this for two reasons:

First, a few weeks ago I spent 4 intensive days with a global customer delivering an AI-assisted development workshop, where I experienced firsthand how Amazon Q CLI boosts developer productivity. During the workshop, the customer was able to add a new feature in their application within a day using Amazon Q CLI, which would have traditionally taken them at least a couple of weeks. With Anthropic’s Claude Sonnet 4.5 in Amazon Q CLI, I know developer productivity will be enhanced further.

Second, I’ve started preparing for my code talk at AWS re:Invent 2025, where my co-speaker and I will show live coding to modernize a legacy codebase using Kiro. I can’t wait to use Anthropic’s Claude Sonnet 4.5 in Kiro to create a live demo. If you want to see this demo and over a thousand other sessions on cloud and AI, join us at AWS re:Invent 2025 in Las Vegas from December 1–5.

Last week’s launches
Here are some launches that got my attention:

• Availability of Claude Sonnet 4.5 in Amazon Bedrock – Anthropic’s most intelligent model, best for coding and complex agents, is now available in Amazon Bedrock. By using Claude Sonnet 4.5 in Amazon Bedrock, developers gain access to a fully managed service that not only provides a unified API for foundation models (FMs) but keeps their data under complete control with enterprise-grade tools for security, and optimization.
• AWS Outposts supports third-party storage integration with Dell and HPE – AWS Outposts third-party storage integration now includes Dell PowerStore and HPE Alletra Storage MP B10000 systems, joining the list of existing integrations with NetApp on-premises enterprise storage arrays and Pure Storage FlashArray. This integration serves three key purposes. First, it helps you maintain your existing storage infrastructure while migrating VMware workloads to AWS. Second, it helps you meet strict data residency requirements by keeping your data on premises while using AWS services. Third, it means you can use AWS Outposts with third-party storage arrays through AWS tooling.
• Amazon ECS Managed Instances now available – Amazon ECS Managed Instances for containerized applications is a new fully managed compute option for Amazon ECS designed to eliminate infrastructure management overhead while giving you access to the full capabilities of Amazon EC2. ECS Managed Instances helps you quickly launch and scale your workloads while enhancing performance and reducing your total cost of ownership.
• Application map is now generally available for Amazon CloudWatch – Amazon CloudWatch now helps you monitor large-scale distributed applications by automatically discovering and organizing services into groups based on configurations and their relationships. With this new application performance monitoring (APM) capability, you can quickly visualize which applications and dependencies to focus on while troubleshooting your distributed applications.
• Amazon Bedrock AgentCore Model Context Protocol (MCP) server now available – With built-in support for runtime, gateway integration, identity management, and agent memory, the AgentCore MCP server is purpose-built to speed up creation of components compatible with Bedrock AgentCore. You can use the AgentCore MCP server for rapid prototyping, production AI solutions, or to scale your agent infrastructure.

Additional Updates
Here are some additional news items and blog posts that I found interesting:

• AWS Builder ID now supports Sign in with Google – You can now create an AWS Builder ID using sign in with Google. AWS Builder ID is a personal profile that provides access to AWS applications including Kiro, AWS Builder Center, AWS Training and Certification, AWS re:Post and AWS Startups.
• AWS API MCP Server v1.0.0 release – AWS API MCP server acts as a bridge between AI assistants and AWS services enabling foundation models to interact with any AWS API through natural language by creating and executing syntactically correct CLI commands. The AWS API MCP Server is open-source and available now on AWS Labs GitHub repository.
• AWS Knowledge MCP Server now generally available – The AWS Knowledge server gives AI agents and MCP clients access to authoritative knowledge, including documentation, blog posts, What’s New announcements, and Well-Architected best practices, in an LLM-compatible format. With this release, the server also includes knowledge about the regional availability of AWS APIs and CloudFormation resources.
• AWS Transform now enables Terraform for VMware network automation – AWS Transform now offers Terraform as an additional option to generate network infrastructure code automatically from VMware environments. The service converts your source network definitions into reusable Terraform modules, complementing current AWS CloudFormation and AWS Cloud Development Kit (CDK) support.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

• AWS AI Agent Global Hackathon – This is your chance to dive deep into our powerful generative AI stack and create something truly awesome. From September 8th to October 20th, you have the opportunity to create AI agents using AWS suite of AI services, competing for over $45,000 in prizes and exclusive go-to-market opportunities.
• AWS Gen AI Lofts – You can learn AWS AI products and services with exclusive sessions, meet industry-leading experts, and have valuable networking opportunities with investors and peers. Register in your nearest city: Paris (October 7–21), London (Oct 13–21), and Tel Aviv (November 11–19).
• AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Munich (October 7), Budapest (October 16).

You can browse all upcoming AWS events and AWS startup events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Prasad

The post AWS Weekly Roundup: Amazon Bedrock, AWS Outposts, Amazon ECS Managed Instances, AWS Builder ID, and more (October 6, 2025) appeared first on The NOKIA Blog.

Three weeks ago, I published a post about the new AWS Region in New Zealand (ap-southeast-6). This led to an incredible opportunity to visit New Zealand, where I met passionate builders and presented at several events including Serverless and Platform Engineering meetup, AWS Tools and Programming meetup, AWS Cloud Clubs in Auckland, and AWS Community Day New Zealand.

During my content creation process for these presentations, I discovered a useful feature in Amazon Q CLI called tangent mode. This feature has transformed how I stay focused by creating conversation checkpoints that let you explore side topics without losing your main thread.

This feature is in experimental mode, and you can enable it with q settings chat.enableTangentMode true. Try it out and see if it helps you.

Last week’s launches
Here are some launches that got my attention:

• New Foundation Models in Amazon Bedrock — Amazon Bedrock expands its model selection with Qwen model family, DeepSeek-V3.1, and Stability AI image services now generally available, giving developers access to powerful multilingual models and advanced image generation capabilities for text generation, code generation, image creation, and complex problem-solving tasks.
• Amazon VPC Reachability Analyzer Expands to Seven New Regions — Network Access Analyzer capabilities are now available in additional regions, helping customers analyze and troubleshoot network connectivity issues across their VPC infrastructure with improved global coverage.
• Amazon Q Developer Supports Remote MCP Servers — Amazon Q Developer now integrates with remote Model Context Protocol (MCP) servers, enabling developers to extend their AI assistant capabilities with custom tools and data sources for enhanced development workflows.
• AWS Step Functions Enhances Distributed Map with New Data Source Options — Step Functions introduces additional data source options and improved observability features for Distributed Map, making it easier to process large-scale parallel workloads with better monitoring and debugging capabilities.
• Amazon Corretto 25 Generally Available — Amazon’s no-cost, multiplatform distribution of OpenJDK 25 is now generally available, providing Java developers with long-term support, performance enhancements, and security updates for building modern applications.
• Amazon SageMaker HyperPod Introduces Autoscaling — SageMaker HyperPod now supports automatic scaling capabilities, allowing machine learning teams to dynamically adjust compute resources based on workload demands, optimizing both performance and cost for distributed training jobs.

Additional Updates

• AWS Named Leader in 2025 Gartner Magic Quadrant for AI Code Assistants – AWS has been recognized as a Leader in Gartner’s Magic Quadrant for AI Code Assistants, highlighting Amazon Q Developer’s capabilities in helping developers write code faster and more securely with AI-powered suggestions.
• Become an AWS Cloud Club Captain – Only a couple of days before it closes! Join a growing network of student cloud enthusiasts by becoming an AWS Cloud Club Captain! As a Captain, you’ll get to organize events and build cloud communities while developing leadership skills. The application window is open September 1-28, 2025.

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events as well as AWS re:Invent and AWS Summits:

• AWS AI Agent Global Hackathon – This is your chance to dive deep into our powerful generative AI stack and create something truly awesome. From September 8th to October 20th, you have the opportunity to create AI agents using AWS suite of AI services, competing for over $45,000 in prizes and exclusive go-to-market opportunities.
• AWS Gen AI Lofts – You can learn AWS AI products and services with exclusive sessions and meet industry-leading experts, and have valuable networking opportunities with investors and peers. Register in your nearest city: Mexico City (September 30–October 2), Paris (October 7–21), London (Oct 13–21), and Tel Aviv (November 11–19).
• AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: South Africa (September 20), Bolivia (September 20), Portugal (September 27), and Manila (October 4-5).

You can browse all upcoming AWS events and AWS startup events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

Happy building!

— Donnie

The post AWS Weekly Roundup: Amazon Q Developer, AWS Step Functions, AWS Cloud Club Captain deadline, and more (September 22, 2025) appeared first on The NOKIA Blog.

A month ago, I shared that Amazon Web Services (AWS) is recognized as a Leader in 2025 Gartner Magic Quadrant for Strategic Cloud Platform Services (SCPS), with Gartner naming AWS a Leader for the fifteenth consecutive year.

In 2024, AWS was named as a Leader in the Gartner Magic Quadrant for AI Code Assistants, Cloud-Native Application Platforms, Cloud Database Management Systems, Container Management, Data Integration Tools, Desktop as a Service (DaaS), and Data Science and Machine Learning Platforms as well as the SCPS. In 2025, we were also recognized as a Leader in the Gartner Magic Quadrant for Contact Center as a Service (CCaaS), Desktop as a Service and Data Science and Machine Learning (DSML) platforms. We strongly believe this means AWS provides the broadest and deepest range of services to customers.

Today, I’m happy to share recent Magic Quadrant reports that named AWS as a Leader in more cloud technology markets: Cloud-Native Application Platforms (aka Cloud Application Platforms) and Container Management.

2025 Gartner Magic Quadrant for Cloud-Native Application Platforms
AWS has been named a Leader in the Gartner Magic Quadrant for Cloud-Native Application Platforms for 2 consecutive years. AWS was positioned highest on “Ability to Execute”. Gartner defines cloud-native application platforms as those that provide managed application runtime environments for applications and integrated capabilities to manage the lifecycle of an application or application component in the cloud environment.

The following image is the graphical representation of the 2025 Magic Quadrant for Cloud-Native Application Platforms.

Our comprehensive cloud-native application portfolio—AWS Lambda, AWS App Runner, AWS Amplify, and AWS Elastic Beanstalk—offers flexible options for building modern applications with strong AI capabilities, demonstrated through continued innovation and deep integration across our broader AWS service portfolio.

You can simplify the service selection through comprehensive documentation, reference architectures, and prescriptive guidance available in the AWS Solutions Library, along with AI-powered, contextual recommendations from Amazon Q based on your specific requirements. While AWS Lambda is optimized for AWS to provide the best possible serverless experience, it follows industry standards for serverless computing and supports common programming languages and frameworks. You can find all necessary capabilities within AWS, including advanced features for AI/ML, edge computing, and enterprise integration.

You can build, deploy, and scale generative AI agents and applications by integrating these compute offerings with Amazon Bedrock for serverless inferences and Amazon SageMaker for artificial intelligence and machine learning (AI/ML) training and management.

Access the complete 2025 Gartner Magic Quadrant for Cloud-Native Application Platforms to learn more.

2025 Gartner Magic Quadrant for Container Management
In the 2025 Gartner Magic Quadrant for Container Management, AWS has been named as a Leader for three years and was positioned furthest for “Completeness of Vision”. Gartner defines container management as offerings that support the deployment and operation of containerized workloads. This process involves orchestrating and overseeing the entire lifecycle of containers, covering deployment, scaling, and operations, to ensure their efficient and consistent performance across different environments.

The following image is the graphical representation of the 2025 Magic Quadrant for Container Management.

AWS container services offer fully managed container orchestration with AWS native solutions and open-source technologies to focus on providing a wide range of deployment options, from Kubernetes to our native orchestrator.

You can use Amazon Elastic Container Service (Amazon ECS) and Amazon Elastic Kubernetes Service (Amazon EKS). Both can be used with AWS Fargate for serverless container deployment. Additionally, EKS Auto Mode simplifies Kubernetes management by automatically provisioning infrastructure, selecting optimal compute instances, and dynamically scaling resources for containerized applications.

You can connect on-premises and edge infrastructure back to AWS container services with EKS Hybrid Nodes and ECS Anywhere, or use EKS Anywhere for a fully disconnected Kubernetes experience supported by AWS. With flexible compute and deployment options, you can reduce operational overhead and focus on innovation and drive business value faster.

Access the complete 2025 Gartner Magic Quadrant for Container Management to learn more.

— Channy

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