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.

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.

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.

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.

My LinkedIn feed was absolutely packed this week with pictures from the AWS Heroes Summit event in Seattle. It was heartwarming to see so many familiar faces and new Heroes coming together.

For those not familiar with the AWS Heroes program, it’s a global community recognition initiative that honors individuals who make outstanding contributions to the AWS community. These Heroes share their deep AWS knowledge through content creation, speaking at events, organizing community gatherings, and contributing to open-source projects.

The AWS Heroes Summit brings these exceptional community leaders together, providing a unique platform for knowledge exchange, networking, and collaboration. As someone who regularly interacts with Heroes through our AWS initiatives, I always find these summits invaluable – they offer deep technical discussions, early access to AWS roadmaps, and opportunities to provide direct feedback to AWS service teams. The insights and connections made at these events often translate into better resources and guidance for the broader AWS community.

Last week’s launches

In addition to this inspiring community, here are some AWS launches that caught my attention:

• AWS expands Internet Protocol v6 (IPv6) support to AWS App Runner, AWS Client VPN, and RDS Data API — Three more AWS services now support IPv6 connectivity, helping you meet compliance requirements and removes the need for handling address translation between IPv4 and IPv6. AWS App Runner now supports IPv6-based inbound and outbound traffic on both public and private App Runner service endpoints. AWS Client VPN announced support for remote access to IPv6 workloads, allowing you to establish secure VPN connections to your IPv6-enabled VPC resources. Finally, RDS Data API now supports IPv6, enabling dual-stack configuration (IPv4 and IPv6) connectivity for your Aurora databases.
• We launched two new instance families this week: the new storage-optimized I8ge and the general-purpose M8i instances —Our I8ge instances, powered by AWS Graviton4 processors, deliver up to 60% better compute performance compared to their Graviton2-based predecessors. These instances feature third-generation AWS Nitro SSDs, providing up to 55% better real-time storage performance per TB and significantly lower I/O latency. With 120 TB of storage and sizes up to 48xlarge (including two metal options), they offer the highest storage density among AWS Graviton-based storage optimized instances. We also launched M8i and M8i-flex instances with custom Intel Xeon 6 processors. These instances deliver up to 15% better price-performance and 2.5x more memory bandwidth than their predecessors. M8i-flex instances are ideal for general-purpose workloads, available from large to 16xlarge. For demanding applications, you can choose from our SAP-certified M8i instances in 13 sizes, including 2 bare metal options and a new 96xlarge size.
• Amazon EC2 Mac Dedicated hosts now support Host Recovery and Reboot-based host maintenance — you can enable two new capabilities for your EC2 Mac Dedicated Hosts: Host Recovery and Reboot-based Host Maintenance. Host Recovery automatically detects potential hardware issues on Mac Dedicated Hosts and seamlessly migrates Mac instances to a new replacement host, minimizing disruption to workloads. Reboot-based Host Maintenance automatically stops and restarts instances on replacement hosts when scheduled maintenance events occur, eliminating the need for manual intervention during planned maintenance windows.
• Amazon Q Developer now supports MCP admin control — Administrators have now the ability to enable or disable the MCP functionality for all the Q Developer clients in their organization. When an administrator disables the functionality, users will not be allowed to add any MCP servers, nor will any previously defined servers be initialized.

Other AWS news

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

• Mastering Amazon Q Developer with Rules — I read an interesting article about Amazon Q Developer’s rules feature this weekend that I want to share with you. What caught my attention is how it solves a pain point I often encounter when working with AI assistants – having to repeatedly explain my coding preferences and standards. With rules, you define your preferences once in Markdown files, and Amazon Q Developer automatically follows them for every interaction. I particularly like how transparent the system is, showing which rules it’s following, and how it helps maintain consistency across teams. Since implementing rules in my projects, I’ve seen more consistent code quality, all while reducing the cognitive load of having to repeatedly explain our standards.
• Strategies for excelling across all four exam domains of the AWS Certified Machine Learning – Specialty certification. The AWS Training & Certification team, where I spent my first three years at AWS, shared how to prepare for the AWS Certified Machine Learning – Specialty certification, whether you’re starting from scratch or building upon existing AWS Certifications. They share the prerequisites and guidance to help you get ready for this certification and demonstrate your expertise in building ML solutions with AWS.
• As is now our tradition after Prime Day, we shared the impressive metrics showing how AWS services scaled to support one of the world’s largest shopping events. Amazon Prime Day 2025 was the biggest ever, setting records for both sales volume and total items sold during the 4-day event. This year was particularly special as we saw a significant transformation in the Prime Day experience through advancements in our generative AI offerings, with customers using Alexa+, Rufus, and AI Shopping Guides to discover deals and get product information. The numbers are staggering – Amazon DynamoDB handled tens of trillions of API calls while maintaining high availability, delivering single-digit millisecond responses and peaking at 151 million requests per second. Amazon API Gateway processed over 1 trillion internal service requests—a 30 percent increase in requests on average per day compared to Prime Day 2024.

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events:

• AWS Summits — Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Toronto (September 4), Los Angeles (September 17), and Bogotá (October 9).
• AWS re:Invent 2025 — This flagship annual conference is coming to Las Vegas from December 1–5. The event catalog is now available. Mark your calendars for this not to be missed gathering of the AWS community.
• 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: Adria (September 5), Baltic (September 10), Aotearoa (September 18), South Africa (September 20), Bolivia (September 20), Portugal (September 27).

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!

— seb

The post AWS Weekly Roundup: Amazon EC2, Amazon Q Developer, IPv6 updates, and more (September 1, 2025) appeared first on The NOKIA Blog.

This week brings an array of innovations spanning from generative AI capabilities to enhancements of foundational services. Whether you’re building AI-powered applications, managing databases, or optimizing your cloud infrastructure, these updates help build more advanced, robust, and flexible applications.

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

• Amazon DocumentDB – Amazon DocumentDB Serverless is now available offering an on-demand, fully managed MongoDB API-compatible document database service. Read more in Channy’s post.
• Amazon Q Developer CLI – You can now create custom agents to help you customize the CLI agent to be more effective when performing specialized tasks such as code reviews and troubleshooting. More info in this blog.
• Amazon Bedrock Data Automation – Now supports DOC/DOCX files for document processing and H.265 encoded video files for video processing, making it easier to build multimodal data analysis pipelines.
• Amazon DynamoDB – Introduced the Amazon DynamoDB data modeling Model Context Protocol (MCP) tool, providing a structured, natural-language-driven workflow to translate application requirements into DynamoDB data models.
• AWS Lambda – Response streaming now supports a default maximum response payload size of 200 MB, 10 times higher than before. Lambda response streaming helps you build applications that progressively stream response payloads back to clients, improving performance for latency sensitive workloads by reducing time to first byte (TTFB) performance.
• Powertools for AWS – Introducing v2 of Powertools for AWS Lambda (Java), a developer toolkit that helps you implement serverless best practices and directly translates AWS Well-Architected recommendations.
• Amazon SNS – Now supports three additional message filtering operators: wildcard matching, anything-but wildcard matching, and anything-but prefix matching. SNS now also supports message group IDs in standard topics, enabling fair queue functionality for subscribed Amazon SQS standard queues.
• Amazon CloudFront – Now offers two capabilities to enhance origin timeout controls: a response completion timeout and support for custom response timeout values for Amazon S3 origins. These capabilities give you more control over how to handle slow or unresponsive origins.
• Amazon EC2 – You are now able to force terminate EC2 instances that are stuck in the shutting-down state.
• Amazon EC2 Auto Scaling – You can now use AWS Lambda functions as notification targets for EC2 Auto Scaling lifecycle hooks. For example, you can use this to trigger custom actions when an instance enters a wait state.
• Amazon SES – You can now provision isolated tenants within a single SES account and apply automated reputation policies to manage email sending.
• AWS Management Console – You can now view your AWS Applications in the Service menu in the console navigation bar. With this view you can see all your Applications and choose an Application to see all its associated resources.
• Amazon Connect – Amazon Connect UI builder now features an updated user interface to reduce the complexity to build structured workflows. It also simplified forecast editing with a new UI experience that improves planning accuracy. The Contact Control Panel now features an updated and more intuitive user interface. Amazon Connect also introduced new actions and workflows into the agent workspace. These actions are powered by third-party applications running in the background.
• AWS Clean Rooms – Now publishes events to Amazon EventBridge for status changes in a Clean Rooms collaboration, further simplifying how companies and their partners analyze and collaborate on their collective datasets without revealing or copying one another’s underlying data.
• AWS Entity Resolution – Introduced rule-based fuzzy matching using Levenshtein Distance, Cosine Similarity, and Soundex algorithms to help resolve consumer records across fragmented, inconsistent, and often incomplete datasets.

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

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

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

AWS Summits — Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Mexico City (August 6) and Jakarta (August 7).

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: Australia (August 15), Adria (September 5), Baltic (September 10), and Aotearoa (September 18).

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

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

– Danilo

The post AWS Weekly Roundup: Amazon DocumentDB, AWS Lambda, Amazon EC2, and more (August 4, 2025) appeared first on The NOKIA Blog.

I’m writing this as I depart from Ho Chi Minh City back to Singapore. Just realized what a week it’s been, so let me rewind a bit. This week, I tried my first Corne keyboard, wrapped up rehearsals for AWS Summit Jakarta with speakers who are absolutely raising the bar, and visited Vietnam to participate as a technical keynote speaker in AWS Community Day Vietnam, an energetic gathering of hundreds of cloud practitioners and AWS enthusiasts who shared knowledge through multiple technical tracks and networking sessions.

What I presented was a keynote titled “Reinvent perspective as modern developers”, featuring serverless, containers, and how we can cut the learning curves and be more productive with Amazon Q Developer and Kiro. I got a chance to discuss with a couple of AWS Community Builders and community developers, who shared how Amazon Q Developer actually addressed their challenges on building applications, with several highlighting significant productivity improvements and smoother learning curves in their cloud development journeys.

As I head back to Singapore, I’m carrying with me not just memories of delicious cà phê sữa đá (iced milk coffee), but also fresh perspectives and inspirations from this vibrant community of cloud innovators.

Introducing Kiro
One of the highlights from last week was definitely Kiro, an AI IDE that helps you deliver from concept to production through a simplified developer experience for working with AI agents. Kiro goes beyond “vibe coding” with features like specs and hooks that help get prototypes into production systems with proper planning and clarity.

Join the waitlist to get notified when it becomes available.

Last week’s AWS Launches
In other news, last week we had AWS Summit in New York, where we released several services. Here are some launches that caught my attention:

• Introducing Amazon Bedrock AgentCore: Securely deploy and operate AI agents at any scale — Amazon Bedrock AgentCore is a comprehensive set of enterprise-grade services that help developers quickly and securely deploy AI agents at scale using any framework and model. It includes AgentCore Runtime, Memory, Observability, Identity, Gateway, Browser, and Code Interpreter services that work together to eliminate infrastructure complexity.
• AWS Free Tier update: New customers can get started and explore AWS with up to $200 in credits — AWS Free Tier now offers enhanced benefits with up to $200 in AWS credits for new customers. You receive $100 upon sign-up and can earn an additional $100 by completing activities with EC2, RDS, Lambda, Bedrock, and AWS Budgets, making it easier to explore AWS services without incurring costs.

• Monitor and debug event-driven applications with new Amazon EventBridge logging — Amazon EventBridge now provides enhanced logging capabilities that offer comprehensive event lifecycle tracking with detailed information about successes, failures, and status codes. This new observability feature addresses microservices and event-driven architecture monitoring challenges by providing visibility into the complete event journey.

• Amazon EKS enables ultra-scale AI/ML workloads with support for 100k nodes per cluster — Amazon EKS now supports up to 100,000 worker nodes in a single cluster, enabling customers to scale up to 1.6 million AWS Trainium accelerators or 800K NVIDIA GPUs. This industry-leading scale empowers customers to train trillion-parameter models and advance AGI development while maintaining Kubernetes conformance and familiar developer experience.

From AWS Builder Center
In case you missed it, we just launched AWS Builder Center and integrated community.aws. Here are my top picks from the posts:

Upcoming AWS events
Check your calendars and sign up for upcoming AWS and AWS Community events:

• AWS re:Invent – Register now to get a head start on choosing your best learning path, booking travel and accommodations, and bringing your team to learn, connect, and have fun. If you’re an early-career professional, you can apply to the All Builders Welcome Grant program, which is designed to remove financial barriers and create diverse pathways into cloud technology.
• AWS Builders Online Series – If you’re based in one of the Asia Pacific time zones, join and learn fundamental AWS concepts, architectural best practices, and hands-on demonstrations to help you build, migrate, and deploy your workloads on AWS.
• AWS Summits — Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Taipei (July 29), Mexico City (August 6), and Jakarta (June 26–27).
• 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: Singapore (August 2), Australia (August 15), Adria (September 5), Baltic (September 10), and Aotearoa (September 18).

You can browse all upcoming AWS led in-person and virtual developer-focused events.

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

— Donnie

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

Join Builder ID: Get started with your AWS Builder journey at builder.aws.com

The post AWS Weekly Roundup: Kiro, AWS Lambda remote debugging, Amazon ECS blue/green deployments, Amazon Bedrock AgentCore, and more (July 21, 2025) appeared first on The NOKIA Blog.

Summer is well and truly here in the UK! I’m a bit of a summer grinch though so, unlike most people, I’m not crazy about “the glorious sun” scorching me when I’m out and about. On the upside, this provides the perfect excuse to retreat to the comfort of a well-ventilated room where I can focus on coding and curating the latest AWS releases to bring you the highlights.

I also managed to escape the heat for most of yesterday while recording an episode for the AWS Developers Podcast where the wonderful Sebastien Stormaq and Tiffany Souterre interviewed me about games development. If you haven’t discovered it yet, I highly recommend you give it a go as the episodes are full of interesting lessons and insights from not just AWS, but customers and community members who share their stories and expertise in a relaxed conversation.

Alright, ready to discover some of the new things we released last week? Here are the highlights.

AWS Builder Center
There is a new home for AWS builders and community members! AWS Builder Center is a new place where cloud builders can connect, share knowledge, and access resources to enhance their AWS journey. The platform enables users to join community programs, discover trending topics, access AWS Skill Builder courses, participate in technical challenges, and more, using a single Builder ID sign-in.

One the features that I’m personally most excited about is the Wishlist. You can now create wishes and tell AWS directly about ways to improve our products and services or share original ideas that you think could help you and your teams. You can also browse and upvote existing wishes to support any suggestions that you think should be prioritized. The AWS teams will keep an eye on this and if a wish has enough traction it may just be considered!

Read the news blog post for a quick tour through some of the most exciting features or head over to AWS Builder Center and start exploring!

AI
The world of AI keeps moving fast and changing our world, by providing new and exciting ways to do things and become more productive. Here are two releases from last week that caught my attention.

• Amazon Q chat in the AWS Management Console can now query AWS service data – Amazon Q Developer expands its capabilities by enabling natural language queries of data stored across AWS services like S3, DynamoDB, and CloudWatch, directly from the AWS Console, Slack, Microsoft Teams, and AWS Console Mobile Application. This enhancement streamlines cloud management and troubleshooting by allowing users to access and analyze service data through conversational interfaces, with access controls managed through IAM permissions.
• Amazon CloudWatch and Application Signals MCP servers for AI-assisted troubleshooting – AWS has released two new Model Context Protocol (MCP) servers – CloudWatch MCP and Application Signals MCP – that enable AI agents to leverage observability data for automated troubleshooting through conversational interfaces. These open-source servers allow AI assistants to analyze metrics, alarms, logs, traces, and service health data across AWS environments, streamlining incident response and root cause analysis without requiring developers to manually navigate multiple AWS consoles.

Oracle Database@AWS
It seems like yesterday when Andy Jassy announced our partnership with Oracle to create Oracle Database@AWS, a jointly offered service that runs Oracle databases on Exadata infrastructure directly within AWS data centers, providing a unified AWS-Oracle experience. Fast forward to last week and Oracle Database@AWS has reached a significant milestone with its general availability release. It is now available in US East (N. Virginia) and US West (Oregon) regions, with plans to expand to 20 additional regions globally.

In addition, VPC Lattice has added support for Oracle Database@AWS enabling seamless connectivity between applications in VPCs and on-premises environments to Oracle database networks. The integration simplifies network management and provides secure access from Oracle Database@AWS to AWS services like Amazon S3 and Amazon Redshift, without requiring complex networking setup.

So if you’re looking to migrate your Oracle database workloads, now is a great time to explore Oracle Database@AWS as it offers a compelling path forward with minimal modifications required.

Additional highlights
Here are some other releases that I think many people will be happy about.

• AWS Config now supports 12 new resource types – AWS Config has expanded its monitoring capabilities with support for 12 new resource types across services including BackupGateway, CloudFront, EntityResolution, Bedrock, and more. These additions are automatically tracked if you have enabled recording for all resource types, enhancing your ability to discover, assess, and audit AWS resources.
• Amazon SageMaker Studio now supports remote connections from Visual Studio Code – Amazon SageMaker Studio now supports remote connections from Visual Studio Code, allowing developers to use their familiar VS Code setup while leveraging SageMaker’s scalable compute resources for AI development.
• AWS Network Firewall: Native AWS Transit Gateway support in all regions – AWS Network Firewall now offers native integration with AWS Transit Gateway across all supported regions, enabling direct attachment and simplified traffic inspection between VPCs and on-premises networks. This integration eliminates the need for managing dedicated VPC subnets and route tables while providing multi-AZ redundancy for improved security and reliability.

Upcoming AWS Events
AWS Summit New York – this is definitely one to watch…literally! Registrations are closed due to capacity but you can tune in to watch live all the announcements and launches! No spoilers, but, trust me, there are a quite a few exciting things in store, so make sure to check it out.

AWS Gen AI Lofts – AWS Gen AI Lofts are multi-day events offering hands-on workshops, expert guidance, and networking opportunities for developers and business leaders looking to explore or advance their generative AI journey. These events are hosted across multiple global locations including San Francisco, Berlin, Dubai, Dublin, Bengaluru, Manchester, Paris, and Tel Aviv, providing accessible opportunities to accelerate your generative AI adoption.

And that’s it for this week! Come back next Monday for more highlights and keep your AWS knowledge up to date as we cover the latest releases.

Matheus Guimaraes | @codingmatheus

The post AWS Weekly Roundup: AWS Builder Center, Amazon Q, Oracle Database@AWS, and more (July 14, 2025) appeared first on The NOKIA Blog.

Every time I visit Seattle, the first thing that greets me at the airport is Mount Rainier. Did you know that the most innovative project at Amazon Web Services (AWS) is named after this mountain?

Project Rainier is a new project to create what is expected to be the world’s most powerful computer for training AI models across multiple data centers in the United Stages. Anthropic will develop the advanced versions of its Claude models with five times more computing power than its current largest training cluster.

The key technology powering Project Rainier is AWS custom-designed Trainium2 chips, which are specialized for the immense data processing required to train complex AI models. Thousands of these Trainium2 chips will be connected in a new type of Amazon EC2 UltraServer and EC2 UltraCluster architecture that allows ultra-fast communication and data sharing across the massive system.

Learn about the AWS vertical integration of Project Rainer, where it designs every component of the technology stack from chips to software, allows it to optimize the entire system for maximum efficiency and reliability.

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

• Amazon S3 access for Amazon FSx for OpenZFS – You can access and analyze your FSx for OpenZFS file data through Amazon S3 Access Points, enabling seamless integration with AWS AI/ML, and analytics services without moving your data out of the file system. You can treat your FSx for OpenZFS data as if it were stored in S3, making it accessible through the S3 API for various applications including Amazon Bedrock, Amazon SageMaker, AWS Glue, and other S3 based cloud-native applications.
• Amazon S3 with sort and z-order compaction for Apache Iceberg tables – You can optimize query performance and reduce costs with new sort and z-order compaction. With S3 Tables, sort compaction automatically organizes data files based on defined column orders, while z-order compaction can be enabled through the maintenance API for efficient multicolumn queries.
• Amazon CloudWatch investigations – You can accelerate your operational troubleshooting in AWS environments using the Amazon CloudWatch AI-powered investigation feature, which helps identify anomalies, surface related signals, and suggest remediation steps. This capability can be initiated through CloudWatch data widgets, multiple AWS consoles, CloudWatch alarm actions, or Amazon Q chat and enables team collaboration and integration with Slack and Microsoft Teams.
• Amazon Bedrock Guardrails Standard tier – You can enhance your AI content safety measures using the new Standard tier. It offers improved content filtering and topic denial capabilities across up to 60 languages, better detection of variations including typos, and stronger protection against prompt attacks. This feature lets you configure safeguards to block harmful content, prevent model hallucinations, redact personally identifiable information (PII), and verify factual claims through automated reasoning checks.
• Amazon Route 53 Resolver endpoints for private hosted zone – You can simplify DNS management across AWS and on-premises infrastructure using the new Route 53 DNS delegation feature for private hosted zone subdomains, which works with both inbound and outbound Resolver endpoints. You can delegate subdomain authority between your on-premises infrastructure and Route 53 Resolver cloud service using name server records, eliminating the need for complex conditional forwarding rules.
• Amazon Q Developer CLI for Java transformation – You can automate and scale Java application upgrades using the new Amazon Q Developer Java transformation command line interface (CLI). This feature perform upgrades from Java versions 8, 11, 17, or 21 to versions 17 or 21 directly from the command line. This tool offers selective transformation options so you can choose specific steps from transformation plans and customize library upgrades.
• New AWS IoT Device Management managed integrations – You can simplify Internet of Things (IoT) device management across multiple manufacturers and protocols using the new managed integrations feature, which provides a unified interface for controlling devices whether they connect directly, through hubs or third-party clouds. The feature includes pre-built cloud-to-cloud (C2C) connectors, device data model templates, and SDKs that support ZigBee, Z-Wave, and Wi-Fi protocols, while you can still create custom connectors and data models.

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

Other AWS news
Various Model Context Protocol (MCP) servers for AWS services have been released. Here are some tutorials about MCP servers that you might find interesting:

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events:

• AWS re:Invent – Register now to get a head start on choosing your best learning path, booking travel and accommodations, and bringing your team to learn, connect, and have fun. If you’re an early-career professional, you can apply to the All Builders Welcome Grant program, which is designed to remove financial barriers and create diverse pathways into cloud technology.
• AWS NY Summits – You can gain insights from Swami’s keynote featuring the latest cutting-edge AWS technologies in compute, storage, and generative AI. My News Blog team is also preparing some exciting news for you. If you’re unable to attend in person, you can still participate by registering for the global live stream. Also, save the date for these upcoming Summits in July and August near your city.
• AWS Builders Online Series – If you’re based in one of the Asia Pacific time zones, join and learn fundamental AWS concepts, architectural best practices, and hands-on demonstrations to help you build, migrate, and deploy your workloads on AWS.

You can browse all upcoming in-person and virtual events.

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

— Channy

The post AWS Weekly Roundup: Project Rainier, Amazon CloudWatch investigations, AWS MCP servers, and more (June 30, 2025) appeared first on The NOKIA Blog.