We’re excited to welcome four outstanding community leaders as our newest AWS Heroes. These individuals embody the spirit of collaboration and knowledge sharing that makes the AWS community thrive. From building AI-powered tools that help fellow builders navigate AWS re:Invent, to leading some of the largest AWS communities in Latin America, to sharing deep cloud architecture expertise across blogs and events. Their dedication to lifting others up through education, mentorship, and community organizing inspires builders around the world.

Damiano Giorgi – Pavia, Italy

Artificial Intelligence Hero Damiano Giorgi is a Cloud Solutions Architect focused on AI and its evolution, who transitioned from on-premises (on-prem) systems engineering to AWS and never looked back. He helps organize AWS User Group Pavia and AWS User Group Milan, and shares content on his personal blog, “Bass and Bytes.” Damiano developed the “Unofficial post:Invent Session Suggester,” powered by Amazon Bedrock and Amazon Nova, to help builders find AWS re:Invent sessions matching their interests. He speaks at conferences across Europe including AWS Summit Milan, AWS Community Day Italy, Adria, Greece, and the Netherlands.

Darryl Ruggles – Ottawa, Canada

Serverless Hero Darryl Ruggles is a Cloud Solutions Architect who spent many years as a software developer before focusing on AWS application and AI/ML architectures. He shares knowledge across serverless, containers, AI/ML, and FinOps through his blog, LinkedIn, and published projects. Darryl is an active member of many online AWS communities including “Believe In Serverless” and loves interacting with the community at events both online and in person.

Ricardo Daniel Ceci – Buenos Aires, Argentina

Artificial Intelligence Hero Ricardo Daniel Ceci leads the AWS User Group Buenos Aires, which is the largest AWS community in Argentina with nearly 2,400 members. He is also the principal organizer of the AWS Community Day Argentina and was recognized as the AWS Community Leader of the Year 2025 for LATAM. Ricardo hosts a podcast featuring conversations with cloud experts, AWS Heroes, and developer advocates across Latin America. He has over fifteen years of experience in cloud and web development, and is passionate about empowering Spanish-speaking builders and lowering the barrier to cloud and AI across LATAM.

Matias Kreder – Buenos Aires, Argentina

Artificial Intelligence Hero Matias Kreder is an AWS Certification Subject Matter Expert (SME) who contributed to multiple AI/ML certifications, including the AWS Certified AI Practitioner exam. His journey began with AWS DeepRacer where he qualified three times as a finalist, which sparked his community work organizing racing events and ML talks across the region. As an AWS User Group Leader for Buenos Aires, he organized the AWS Community Day Argentina 2025 and speaks at community events throughout LATAM.

Learn More

Visit the AWS Heroes webpage if you’d like to learn more about the AWS Heroes program, or to connect with a Hero near you.

— Taylor

The post Meet Our Newest AWS Heroes – May 2026 appeared first on The NOKIA Blog.

Hello! I’m Daniel Abib, and this is my first AWS Weekly Roundup. I’m a Senior Specialist Solutions Architect at AWS, focused on the generative AI and Amazon Bedrock. With over 28 years of experience in solution architecture, software development, and cloud architecture, I help Startups & Enterprises harness the power of generative AI with Amazon Bedrock. I’ve been at AWS for more than six and a half years, working closely with customers across Latin America, and I’m also passionate about Serverless technologies.

Outside of work and endurance sports, I’m a dedicated father to Cecília (7) and Rafael (4), who keep me busier—and happier— than any distributed system ever could. I’m based in São Paulo, you can find me on LinkedIn and X (@DCABib), where I share insights about generative AI, Amazon Bedrock, AWS serverless services, and the occasional Ironman throwback.

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

Last week’s launches

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

• Amazon Redshift increases performance for new queries in dashboards and ETL workloads by up to 7x — Amazon Redshift now delivers up to 7x faster performance for new queries in dashboards and ETL workloads. Queries you run for the first time — without cached results — now execute significantly faster, reducing wait times for interactive dashboards and accelerating your ETL pipelines. This is particularly impactful for workloads with high query variability where cache hits are less frequent.
• NVIDIA Nemotron 3 Super now available on Amazon Bedrock — NVIDIA Nemotron 3 Super is now available in Amazon Bedrock, expanding the lineup of foundation models you can access through the unified Bedrock API. Nemotron 3 Super is a high-performance language model optimized for tasks such as text generation, complex reasoning, summarization, and code generation. You can now invoke Nemotron 3 Super alongside other foundation models in your existing Bedrock workflows, without managing any infrastructure.
• Introducing Nova Forge SDK, a seamless way to customize Nova models for enterprise AI — Nova Forge SDK provides a streamlined way to fine-tune and customize Amazon Nova models for enterprise use cases. You can adapt Nova models to your domain-specific data and deploy them directly within Amazon Bedrock, reducing the complexity of building tailored AI solutions. The SDK handles the heavy lifting of model customization, letting you focus on your business logic rather than the underlying infrastructure.
• Amazon Corretto 26 is now generally available — Amazon Corretto 26, the latest long-term support (LTS) release of the no-cost, production-ready distribution of OpenJDK, is now generally available. Corretto 26 includes the latest Java language features, performance improvements, and security patches, all backed by long-term support from AWS. You can use it across development and production environments on Amazon Linux, Windows, macOS, and Docker images.
• AWS Lambda now supports Availability Zone metadata — AWS Lambda now provides Availability Zone metadata for your function invocations. You can now identify which Availability Zone your Lambda function is running in, enabling better observability, more informed architectural decisions, and simplified troubleshooting for latency-sensitive and multi-AZ workloads. This is particularly useful when correlating Lambda execution with other AZ-aware services in your architecture.
• Amazon CloudWatch Logs now supports log ingestion using HTTP-based protocol — Amazon CloudWatch Logs now supports ingesting logs using an HTTP-based protocol, making it simpler to send logs from applications and services that use standard HTTP endpoints. You can now route logs to CloudWatch Logs without requiring custom agents or additional SDK integrations, lowering the barrier to centralized log management across your workloads.
• Amazon EKS announces 99.99% Service Level Agreement and new 8XL scaling tier for Provisioned Control Plane clusters — Amazon EKS now offers a 99.99% Service Level Agreement (SLA) for clusters running on Provisioned Control Plane, up from the 99.95% SLA offered on standard control plane. EKS is also introducing the 8XL scaling tier, the largest available Provisioned Control Plane tier, which doubles the Kubernetes API server request processing capacity of the next lower 4XL tier — ideal for large-scale workloads like AI/ML training, high-performance computing (HPC), and large-scale data processing.

Other AWS news

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

• Kiro for students — Kiro is now available for students, giving the next generation of builders access to AI-powered development tools at no cost. As Swami Sivasubramanian shared on LinkedIn, “Students are the future decision-makers shaping technology” — and Kiro gives them hands-on experience building with AI from day one. If you’re a student or know someone who is, this is a great opportunity to start building with AI-assisted development.
• Strands Steering Hooks achieved 100% agent accuracy — The Strands Agents team published results showing that Steering Hooks can achieve 100% agent accuracy, outperforming both prompt engineering and rigid workflow approaches for controlling agent behavior. As Swami highlighted on LinkedIn, building reliable AI agents often means rethinking how we guide model behavior — and Steering Hooks offer a compelling new path to agent reliability.
• Introducing Badges on AWS Builder Center — AWS Builder Center now features badges that recognize your contributions and achievements within the builder community. You can earn badges by sharing solutions, participating in challenges, and engaging with fellow builders. It’s a great way to showcase your expertise and track your growth.
• Keep Building Together: The Power of Community — A thoughtful read on the power of community-driven learning and collaboration in the AWS ecosystem. Whether you’re just getting started with AWS or you’ve been building for years, the builder community is a place to connect, share knowledge, and grow together. I highly recommend checking it out.

Upcoming AWS events

Check your calendar and sign up for upcoming AWS events:

• AWS Summits — Join AWS Summits in 2026, free in-person events where you can explore emerging cloud and AI technologies, learn best practices, and network with industry peers and experts. Upcoming Summits include Paris (April 1), London (April 22), Bengaluru (April 23–24), Singapore (May 6), Tel Aviv (May 6), and Stockholm (May 7).
• AWS Community Days — Community-led conferences where content is planned, sourced, and delivered by community leaders, featuring technical discussions, workshops, and hands-on labs. Upcoming events include San Francisco (April 10) and Romania (April 23–24).
• AWSome Women Summit LATAM — Taking place on March 28 in Mexico City, this event celebrates and empowers women in cloud technology across Latin America. A fantastic initiative for the LATAM tech community.

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse the AWS Events and Webinars for upcoming AWS-led in-person and virtual events and developer-focused events.

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

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: NVIDIA Nemotron 3 Super on Amazon Bedrock, Nova Forge SDK, Amazon Corretto 26, and more (March 23, 2026) 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, my team met many developers at Developer Week in San Jose. My colleague, Vinicius Senger delivered a great keynote about renascent software—a new way of building and evolving applications where humans and AI collaborate as co-developers using Kiro. Other colleagues spoke about building and deploying production-ready AI agents. Everyone stayed to ask and hear the questions related to agent memory, multi-agent patterns, meta-tooling and hooks. It was interesting how many developers were actually building agents.

We are continuing to meet developers and hear their feedback at third-party developer conferences. You can meet us at the dev/nexus, the largest and longest-running Java ecosystem conference on March 4-6 in Atlanta. My colleague, James Ward will speak about building AI Agents with Spring and MCP, and Vinicius Senger and Jonathan Vogel will speak about 10 tools and tips to upgrade your Java code with AI. I’ll keep sharing places for you to connect with us.

Last week’s launches

Here are some of the other announcements from last week:

• Claude Sonnet 4.6 model in Amazon Bedrock – You can now use Claude Sonnet 4.6 which offers frontier performance across coding, agents, and professional work at scale. Claude Sonnet 4.6 approaches Opus 4.6 intelligence at a lower cost. It enables faster, high-quality task completion, making it ideal for high-volume coding and knowledge work use cases.
• Amazon EC2 Hpc8a instances powered by 5th Gen AMD EPYC processors – You can use new Hpc8a instances delivering up to 40% higher performance, increased memory bandwidth, and 300 Gbps Elastic Fabric Adapter networking. You can accelerate compute-intensive simulations, engineering workloads, and tightly coupled HPC applications.
• Amazon SageMaker Inference for custom Amazon Nova models – You can now configure the instance types, auto-scaling policies, and concurrency settings for custom Nova model deployments with Amazon SageMaker Inference to best meet your needs.
• Nested virtualization on virtual Amazon EC2 instances – You can create nested virtual machines by running KVM or Hyper-V on virtual EC2 instances. You can leverage this capability for use cases such as running emulators for mobile applications, simulating in-vehicle hardware for automobiles, and running Windows Subsystem for Linux on Windows workstations.
• Server-Side Encryption by default in Amazon Aurora – Amazon Aurora further strengthens your security posture by automatically applying server-side encryption by default to all new databases clusters using AWS-owned keys. This encryption is fully managed, transparent to users, and with no cost or performance impact.
• Kiro in AWS GovCloud (US) Regions – You can use Kiro for the development teams behind government missions. Developers in regulated environments can now leverage Kiro’s agentic AI tool with the rigorous security controls required.

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

Additional updates

Here are some additional news items that you might find interesting:

• Introducing Agent Plugins for AWS – You can see how new open-source Agent Plugins for AWS extend coding agents with skills for deploying applications to AWS. Using the deploy-on-aws plugin, you can generate architecture recommendations, cost estimates, and infrastructure-as-code directly from your coding agent.
• A chat with Byron Cook on automated reasoning and trust in AI systems – You can hear how to verify AI systems doing the right thing using automated reasoning when they generate code or manage critical decisions. Byron Cook’s team has spent a decade proving correctness in AWS and apply those techniques to agentic systems.
• Best practices for deploying AWS DevOps Agent in production – You can read best practices for setting up DevOps Agent Spaces that balance investigation capability with operational efficiency. According to Swami Sivasubramanian, AWS DevOps Agent, a frontier agent that resolves and proactively prevents incidents, has handled thousands of escalations, with an estimated root cause identification rate of over 86% within Amazon.

From AWS community

Here are my personal favorite posts from AWS community:

Join the AWS Builder Center to connect with community, share knowledge, and access content that supports your development.

Upcoming AWS events

Check your calendar and sign up for upcoming AWS events:

• AWS Summits – Join AWS Summits in 2026, free in-person events where you can explore emerging cloud and AI technologies, learn best practices, and network with industry peers and experts. Upcoming Summits include Paris (April 1), London (April 22), and Bengaluru (April 23–24).
• Amazon Nova AI Hackathon – Join developers worldwide to build innovative generative AI solutions using frontier foundation models and compete for $40,000 in prizes across five categories including agentic AI, multimodal understanding, UI automation, and voice experiences during this six-week challenge from February 2nd to March 16th, 2026.
• AWS Community Days – Community-led conferences where content is planned, sourced, and delivered by community leaders, featuring technical discussions, workshops, and hands-on labs. Upcoming events include Ahmedabad (February 28), JAWS Days in Tokyo (March 7), Chennai (March 7), Slovakia (March 11), and Pune (March 21).

Browse here for upcoming AWS led in-person and virtual events, startup events, and developer-focused events.

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

— Channy

The post AWS Weekly Roundup: Claude Sonnet 4.6 in Amazon Bedrock, Kiro in GovCloud Regions, new Agent Plugins, and more (February 23, 2026) appeared first on The NOKIA Blog.

Since we launched Amazon Nova customization in Amazon SageMaker AI at AWS NY Summit 2025, customers have been asking for the same capabilities with Amazon Nova as they do when they customize open weights models in Amazon SageMaker Inference. They also wanted have more control and flexibility in custom model inference over instance types, auto-scaling policies, context length, and concurrency settings that production workloads demand.

Today, we’re announcing the general availability of custom Nova model support in Amazon SageMaker Inference, a production-grade, configurable, and cost-efficient managed inference service to deploy and scale full-rank customized Nova models. You can now experience an end-to-end customization journey to train Nova Micro, Nova Lite, and Nova 2 Lite models with reasoning capabilities using Amazon SageMaker Training Jobs or Amazon HyperPod and seamlessly deploy them with managed inference infrastructure of Amazon SageMaker AI.

With Amazon SageMaker Inference for custom Nova models, you can reduce inference cost through optimized GPU utilization using Amazon Elastic Compute Cloud (Amazon EC2) G5 and G6 instances over P5 instances, auto-scaling based on 5-minute usage patterns, and configurable inference parameters. This feature enables deployment of customized Nova models with continued pre-training, supervised fine-tuning, or reinforcement fine-tuning for your use cases. You can also set advanced configurations about context length, concurrency, and batch size for optimizing the latency-cost-accuracy tradeoff for your specific workloads.

Let’s see how to deploy customized Nova models on SageMaker AI real-time endpoints, configure inference parameters, and invoke your models for testing.

Deploy custom Nova models in SageMaker Inference

At AWS re:Invent 2025, we introduced new serverless customization in Amazon SageMaker AI for popular AI models including Nova models. With a few clicks, you can seamlessly select a model and customization technique, and handle model evaluation and deployment. If you already have a trained custom Nova model artifact, you can deploy the models on SageMaker Inference through the SageMaker Studio or SageMaker AI SDK.

In the SageMaker Studio, choose a trained Nova model in Models in your models in the Models menu. You can deploy the model by choosing Deploy button, SageMaker AI and Create new endpoint.

Choose the endpoint name, instance type, and advanced options such as instance count, max instance count, permission and networking, and Deploy button. At GA launch, you can use g5.12xlarge, g5.24xlarge, g5.48xlarge, g6.12xlarge, g6.24xlarge, g6.48xlarge, and p5.48xlarge instance types for the Nova Micro model, g5.48xlarge, g6.48xlarge, and p5.48xlarge for the Nova Lite model, and p5.48xlarge for the Nova 2 Lite model.

Creating your endpoint requires time to provision the infrastructure, download your model artifacts, and initialize the inference container.

After model deployment completes and the endpoint status shows InService, you can perform real-time inference using the new endpoint. To test the model, choose the Playground tab and input your prompt in the Chat mode.

You can also use the SageMaker AI SDK to create two resources: a SageMaker AI model object that references your Nova model artifacts, and an endpoint configuration that defines how the model will be deployed.

The following code sample creates a SageMaker AI model that references your Nova model artifacts. For supported container images by Region, refer table lists the container image URIs:

# Create a SageMaker AI model
    model_response = sagemaker.create_model(
        ModelName= 'Nova-micro-ml-g5-12xlarge',
        PrimaryContainer={
            'Image': '708977205387.dkr.ecr.us-east-1.amazonaws.com/nova-inference-repo:v1.0.0',
            'ModelDataSource': {
                'S3DataSource': {
                   'S3Uri': 's3://your-bucket-name/path/to/model/artifacts/',
                   'S3DataType': 'S3Prefix',
                   'CompressionType': 'None'
                }
            },
            # Model Parameters
            'Environment': {
                'CONTEXT_LENGTH': 8000,
                'MAX_CONCURRENCY': 16,
                'DEFAULT_TEMPERATURE': 0.0,
                'DEFAULT_TOP_P': 1.0
            }
        },
        ExecutionRoleArn=SAGEMAKER_EXECUTION_ROLE_ARN,
        EnableNetworkIsolation=True
    )
    print("Model created successfully!")

Next, create an endpoint configuration that defines your deployment infrastructure and deploy your Nova model by creating a SageMaker AI real-time endpoint. This endpoint will host your model and provide a secure HTTPS endpoint for making inference requests.

# Create Endpoint Configuration
    production_variant = {
        'VariantName': 'primary',
        'ModelName': 'Nova-micro-ml-g5-12xlarge',
        'InitialInstanceCount': 1,
        'InstanceType': 'ml.g5.12xlarge',
    }
    
    config_response = sagemaker.create_endpoint_config(
        EndpointConfigName= 'Nova-micro-ml-g5-12xlarge-Config',
        ProductionVariants= production_variant
    )
    print("Endpoint configuration created successfully!")
    
# Deploy your Noval model
    endpoint_response = sagemaker.create_endpoint(
        EndpointName= 'Nova-micro-ml-g5-12xlarge-endpoint',
        EndpointConfigName= 'Nova-micro-ml-g5-12xlarge-Config'
    )
    print("Endpoint creation initiated successfully!")

After the endpoint is created, you can send inference requests to generate predictions from your custom Nova model. Amazon SageMaker AI supports synchronous endpoints for real-time with streaming/non-streaming modes and asynchronous endpoints for batch processing.

For example, the following code creates streaming completion format for text generation:

# Streaming chat request with comprehensive parameters
streaming_request = {
"messages": [
        {"role": "user", "content": "Compare our Q4 2025 actual spend against budget across all departments and highlight variances exceeding 10%"}
    ],
    "max_tokens": 512,
    "stream": True,
    "temperature": 0.7,
    "top_p": 0.95,
    "top_k": 40,
    "logprobs": True,
    "top_logprobs": 2,
    "reasoning_effort": "low",  # Options: "low", "high"
    "stream_options": {"include_usage": True}
}

invoke_nova_endpoint(streaming_request)

def invoke_nova_endpoint(request_body):
"""
    Invoke Nova endpoint with automatic streaming detection.
    
    Args:
        request_body (dict): Request payload containing prompt and parameters
    
    Returns:
        dict: Response from the model (for non-streaming requests)
        None: For streaming requests (prints output directly)
    """
    body = json.dumps(request_body)
    is_streaming = request_body.get("stream", False)
    
    try:
        print(f"Invoking endpoint ({'streaming' if is_streaming else 'non-streaming'})...")
        
        if is_streaming:
            response = runtime_client.invoke_endpoint_with_response_stream(
                EndpointName=ENDPOINT_NAME,
                ContentType='application/json',
                Body=body
            )
            
            event_stream = response['Body']
            for event in event_stream:
                if 'PayloadPart' in event:
                    chunk = event['PayloadPart']
                    if 'Bytes' in chunk:
                        data = chunk['Bytes'].decode()
                        print("Chunk:", data)
        else:
            # Non-streaming inference
            response = runtime_client.invoke_endpoint(
                EndpointName=ENDPOINT_NAME,
                ContentType='application/json',
                Accept='application/json',
                Body=body
            )
            
            response_body = response['Body'].read().decode('utf-8')
            result = json.loads(response_body)
            print("✅ Response received successfully")
            return result
    
    except ClientError as e:
        error_code = e.response['Error']['Code']
        error_message = e.response['Error']['Message']
        print(f"❌ AWS Error: {error_code} - {error_message}")
    except Exception as e:
        print(f"❌ Unexpected error: {str(e)}")

To use full code examples, visit Getting started with customizing Nova models on SageMaker AI. To learn more about best practices on deploying and managing models, visit Best practices for SageMaker AI.

Now available

Amazon SageMaker Inference for custom Nova models is available today in US East (N. Virginia) and US West (Oregon) AWS Regions. For Regional availability and a future roadmap, visit the AWS Capabilities by Region.

The feature supports Nova Micro, Nova Lite, and Nova 2 Lite models with reasoning capabilities, running on EC2 G5, G6, and P5 instances with auto-scaling support. You pay only for the compute instances you use, with per-hour billing and no minimum commitments. For more information, visit Amazon SageMaker AI Pricing page.

Give it a try in Amazon SageMaker AI console and send feedback to AWS re:Post for SageMaker or through your usual AWS Support contacts.

— Channy

The post Announcing Amazon SageMaker Inference for custom Amazon Nova models appeared first on The NOKIA Blog.

Organizations face a challenging trade-off when adapting AI models to their specific business needs: settle for generic models that produce average results, or tackle the complexity and expense of advanced model customization. Traditional approaches force a choice between poor performance with smaller models or the high costs of deploying larger model variants and managing complex infrastructure. Reinforcement fine-tuning is an advanced technique that trains models using feedback instead of massive labeled datasets, but implementing it typically requires specialized ML expertise, complicated infrastructure, and significant investment—with no guarantee of achieving the accuracy needed for specific use cases.

Today, we’re announcing reinforcement fine-tuning in Amazon Bedrock, a new model customization capability that creates smarter, more cost-effective models that learn from feedback and deliver higher-quality outputs for specific business needs. Reinforcement fine-tuning uses a feedback-driven approach where models improve iteratively based on reward signals, delivering 66% accuracy gains on average over base models.

Amazon Bedrock automates the reinforcement fine-tuning workflow, making this advanced model customization technique accessible to everyday developers without requiring deep machine learning (ML) expertise or large labeled datasets.

How reinforcement fine-tuning works

Reinforcement fine-tuning is built on top of reinforcement learning principles to address a common challenge: getting models to consistently produce outputs that align with business requirements and user preferences.

While traditional fine-tuning requires large, labeled datasets and expensive human annotation, reinforcement fine-tuning takes a different approach. Instead of learning from fixed examples, it uses reward functions to evaluate and judge which responses are considered good for particular business use cases. This teaches models to understand what makes a quality response without requiring massive amounts of pre-labeled training data, making advanced model customization in Amazon Bedrock more accessible and cost-effective.

Here are the benefits of using reinforcement fine-tuning in Amazon Bedrock:

• Ease of use – Amazon Bedrock automates much of the complexity, making reinforcement fine-tuning more accessible to developers building AI applications. Models can be trained using existing API logs in Amazon Bedrock or by uploading datasets as training data, eliminating the need for labeled datasets or infrastructure setup.
• Better model performance – Reinforcement fine-tuning improves model accuracy by 66% on average over base models, enabling optimization for price and performance by training smaller, faster, and more efficient model variants. This works with Amazon Nova 2 Lite model, improving quality and price performance for specific business needs, with support for additional models coming soon.
• Security – Data remains within the secure AWS environment throughout the entire customization process, mitigating security and compliance concerns.

The capability supports two complementary approaches to provide flexibility for optimizing models:

• Reinforcement Learning with Verifiable Rewards (RLVR) uses rule-based graders for objective tasks like code generation or math reasoning.
• Reinforcement Learning from AI Feedback (RLAIF) employs AI-based judges for subjective tasks like instruction following or content moderation.

Getting started with reinforcement fine-tuning in Amazon Bedrock

Let’s walk through creating a reinforcement fine-tuning job.

First, I access the Amazon Bedrock console. Then, I navigate to the Custom models page. I choose Create and then choose Reinforcement fine-tuning job.

I start by entering the name of this customization job and then select my base model. At launch, reinforcement fine-tuning supports Amazon Nova 2 Lite, with support for additional models coming soon.

Next, I need to provide training data. I can use my stored invocation logs directly, eliminating the need to upload separate datasets. I can also upload new JSONL files or select existing datasets from Amazon Simple Storage Service (Amazon S3). Reinforcement fine-tuning automatically validates my training dataset and supports the OpenAI Chat Completions data format. If I provide invocation logs in the Amazon Bedrock invoke or converse format, Amazon Bedrock automatically converts them to the Chat Completions format.

The reward function setup is where I define what constitutes a good response. I have two options here. For objective tasks, I can select Custom code and write custom Python code that gets executed through AWS Lambda functions. For more subjective evaluations, I can select Model as judge to use foundation models (FMs) as judges by providing evaluation instructions.

Here, I select Custom code, and I create a new Lambda function or use an existing one as a reward function. I can start with one of the provided templates and customize it for my specific needs.

I can optionally modify default hyperparameters like learning rate, batch size, and epochs.

For enhanced security, I can configure virtual private cloud (VPC) settings and AWS Key Management Service (AWS KMS) encryption to meet my organization’s compliance requirements. Then, I choose Create to start the model customization job.

During the training process, I can monitor real-time metrics to understand how the model is learning. The training metrics dashboard shows key performance indicators including reward scores, loss curves, and accuracy improvements over time. These metrics help me understand whether the model is converging properly and if the reward function is effectively guiding the learning process.

When the reinforcement fine-tuning job is completed, I can see the final job status on the Model details page.

Once the job is completed, I can deploy the model with a single click. I select Set up inference, then choose Deploy for on-demand.

Here, I provide a few details for my model.

After deployment, I can quickly evaluate the model’s performance using the Amazon Bedrock playground. This helps me to test the fine-tuned model with sample prompts and compare its responses against the base model to validate the improvements. I select Test in playground.

The playground provides an intuitive interface for rapid testing and iteration, helping me confirm that the model meets my quality requirements before integrating it into production applications.

Interactive demo

Learn more by navigating an interactive demo of Amazon Bedrock reinforcement fine-tuning in action.

Additional things to know

Here are key points to note:

• Templates — There are seven ready-to-use reward function templates covering common use cases for both objective and subjective tasks.
• Pricing — To learn more about pricing, refer to the Amazon Bedrock pricing page.
• Security — Training data and custom models remain private and aren’t used to improve FMs for public use. It supports VPC and AWS KMS encryption for enhanced security.

Get started with reinforcement fine-tuning by visiting the reinforcement fine-tuning documentation and by accessing the Amazon Bedrock console.

Happy building!

— Donnie

The post Amazon Bedrock adds reinforcement fine-tuning simplifying how developers build smarter, more accurate AI models appeared first on The NOKIA Blog.

Today, I’m happy to announce new serverless customization in Amazon SageMaker AI for popular AI models, such as Amazon Nova, DeepSeek, GPT-OSS, Llama, and Qwen. The new customization capability provides an easy-to-use interface for the latest fine-tuning techniques like reinforcement learning, so you can accelerate the AI model customization process from months to days.

With a few clicks, you can seamlessly select a model and customization technique, and handle model evaluation and deployment—all entirely serverless so you can focus on model tuning rather than managing infrastructure. When you choose serverless customization, SageMaker AI automatically selects and provisions the appropriate compute resources based on the model and data size.

Getting started with serverless model customization

You can get started customizing models in Amazon SageMaker Studio. Choose Models in the left navigation pane and check out your favorite AI models to be customized.

Customize with UI

You can customize AI models in a only few clicks. In the Customize model dropdown list for a specific model such as Meta Llama 3.1 8B Instruct, choose Customize with UI.

You can select a customization technique used to adapt the base model to your use case. SageMaker AI supports Supervised Fine-Tuning and the latest model customization techniques including Direct Preference Optimization, Reinforcement Learning from Verifiable Rewards (RLVR), and Reinforcement Learning from AI Feedback (RLAIF). Each technique optimizes models in different ways, with selection influenced by factors such as dataset size and quality, available computational resources, task at hand, desired accuracy levels, and deployment constraints.

Upload or select a training dataset to match the format required by the customization technique selected. Use the values of batch size, learning rate, and number of epochs recommended by the technique selected. You can configure advanced settings such as hyperparameters, a newly introduced serverless MLflow application for experiment tracking, and network and storage volume encryption. Choose Submit to get started on your model training job.

After your training job is complete, you can see the models you created in the My Models tab. Choose View details in one of your models.

By choosing Continue customization, you can continue to customize your model by adjusting hyperparameters or training with different techniques. By choosing Evaluate, you can evaluate your customized model to see how it performs compared to the base model.

When you complete both jobs, you can choose either the SageMaker or Bedrock in the Deploy dropdown list to deploy your model.

You can choose Amazon Bedrock for serverless inference. Choose Bedrock and the model name to deploy the model into Amazon Bedrock. To find your deployed models, choose Imported models in the Bedrock console.

You can also deploy your model to a SageMaker AI inference endpoint if you want to control your deployment resources such as an instance type and instance count. After the SageMaker AI deployment is In service, you can use this endpoint to perform inference. In the Playground tab, you can test your customized model with a single prompt or chat mode.

With the serverless MLflow capability, you can automatically log all critical experiment metrics without modifying code and access rich visualizations for further analysis.

Customize with code

When you choose customizing with code, you can see a sample notebook to fine-tune or deploy AI models. If you want to edit the sample notebook, open it in JupyterLab. Alternatively, you can deploy the model immediately by choosing Deploy.

You can choose the Amazon Bedrock or SageMaker AI endpoint by selecting the deployment resources either from Amazon SageMaker Inference or Amazon SageMaker Hyperpod.

When you choose Deploy on the bottom right of the page, it will be redirected back to the model detail page. After the SageMaker AI deployment is in service, you can use this endpoint to perform inference.

Okay, you’ve seen how to streamline the model customization in the SageMaker AI. You can now choose your favorite way. To learn more, visit the Amazon SageMaker AI Developer Guide.

Now available

New serverless AI model customization in Amazon SageMaker AI is now available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland) Regions. You only pay for the tokens processed during training and inference. To learn more details, visit Amazon SageMaker AI pricing page.

Give it a try in Amazon SageMaker Studio and send feedback to AWS re:Post for SageMaker or through your usual AWS Support contacts.

— Channy

The post New serverless customization in Amazon SageMaker AI accelerates model fine-tuning appeared first on The NOKIA Blog.

Today, we’re announcing two new AI model training features within Amazon SageMaker HyperPod: checkpointless training, an approach that mitigates the need for traditional checkpoint-based recovery by enabling peer-to-peer state recovery, and elastic training, enabling AI workloads to automatically scale based on resource availability.

• Checkpointless training – Checkpointless training eliminates disruptive checkpoint-restart cycles, maintaining forward training momentum despite failures, reducing recovery time from hours to minutes. Accelerate your AI model development, reclaim days from development timelines, and confidently scale training workflows to thousands of AI accelerators.
• Elastic training  – Elastic training maximizes cluster utilization as training workloads automatically expand to use idle capacity as it becomes available, and contract to yield resources as higher-priority workloads like inference volumes peak. Save hours of engineering time per week spent reconfiguring training jobs based on compute availability.

Rather than spending time managing training infrastructure, these new training techniques mean that your team can concentrate entirely on enhancing model performance, ultimately getting your AI models to market faster. By eliminating the traditional checkpoint dependencies and fully utilizing available capacity, you can significantly reduce model training completion times.

Checkpointless training: How it works

Traditional checkpoint-based recovery has these sequential job stages: 1) job termination and restart, 2) process discovery and network setup, 3) checkpoint retrieval, 4) data loader initialization, and 5) training loop resumption. When failures occur, each stage can become a bottleneck and training recovery can take up to an hour on self-managed training clusters. The entire cluster must wait for every single stage to complete before training can resume. This can lead to the entire training cluster sitting idle during recovery operations, which increases costs and extends the time to market.

Checkpointless training removes this bottleneck entirely by maintaining continuous model state preservation across the training cluster. When failures occur, the system instantly recovers by using healthy peers, avoiding the need for a checkpoint-based recovery that requires restarting the entire job. As a result, checkpointless training enables fault recovery in minutes.

Checkpointless training is designed for incremental adoption and built on four core components that work together: 1) collective communications initialization optimizations, 2) memory-mapped data loading that enables caching, 3) in-process recovery, and 4) checkpointless peer-to-peer state replication. These components are orchestrated through the HyperPod training operator that is used to launch the job. Each component optimizes a specific step in the recovery process, and together they enable automatic detection and recovery of infrastructure faults in minutes with zero manual intervention, even with thousands of AI accelerators. You can progressively enable each of these features as your training scales.

The latest Amazon Nova models were trained using this technology on tens of thousands of accelerators. Additionally, based on internal studies on cluster sizes ranging between 16 GPUs to over 2,000 GPUs, checkpointless training showcased significant improvements in recovery times, reducing downtime by over 80% compared to traditional checkpoint-based recovery.

To learn more, visit checkpointless training GitHub page for implementation and HyperPod Checkpointless Training in the Amazon SageMaker AI Developer Guide.

Elastic training: How it works

On clusters that run different types of modern AI workloads, accelerator availability can change continuously throughout the day as short-duration training runs complete, inference spikes occur and subside, or resources free up from completed experiments. Despite this dynamic availability of AI accelerators, traditional training workloads remain locked into their initial compute allocation, unable to take advantage of idle accelerators without manual intervention. This rigidity leaves valuable GPU capacity unused and prevents organizations from maximizing their infrastructure investment.

Elastic training transforms how training workloads interact with cluster resources. Training jobs can automatically scale up to utilize available accelerators and gracefully contract when resources are needed elsewhere, all while maintaining training quality.

Workload elasticity is enabled through the HyperPod training operator that orchestrates scaling decisions through integration with the Kubernetes control plane and resource scheduler. It continuously monitors cluster state through three primary channels: pod lifecycle events, node availability changes, and resource scheduler priority signals. This comprehensive monitoring enables near-instantaneous detection of scaling opportunities, whether from newly available resources or requests from higher-priority workloads.

The scaling mechanism relies on adding and removing data parallel replicas. When additional compute resources become available, new data parallel replicas join the training job, accelerating throughput. Conversely, during scale-down events (for example, when a higher-priority workload requests resources), the system scales down by removing replicas rather than terminating the entire job, allowing training to continue at reduced capacity.

Across different scales, the system preserves the global batch size and adapts learning rates, preventing model convergence from being adversely impacted. This enables workloads to dynamically scale up or down to utilize available AI accelerators without any manual intervention.

You can start elastic training through the HyperPod recipes for publicly available foundation models (FMs) including Llama and GPT-OSS. Additionally, you can modify your PyTorch training scripts to add elastic event handlers, which enable the job to dynamically scale.

To learn more, visit the HyperPod Elastic Training in the Amazon SageMaker AI Developer Guide. To get started, find the HyperPod recipes available in the AWS GitHub repository.

Now available

Both features are available in all the Regions in which Amazon SageMaker HyperPod is available. You can use these training techniques without additional cost. To learn more, visit the SageMaker HyperPod product page and SageMaker AI pricing page.

Give it a try and send feedback to AWS re:Post for SageMaker or through your usual AWS Support contacts.

— Channy

The post Introducing checkpointless and elastic training on Amazon SageMaker HyperPod appeared first on The NOKIA Blog.

The weeks before AWS re:Invent, my team is full steam ahead preparing content for the conference. I can’t wait to meet you at one of my three talks: CMP346 : Supercharge AI/ML on Apple Silicon with EC2 Mac, CMP344: Speed up Apple application builds with CI/CD on EC2 Mac, and DEV416: Develop your AI Agents and MCP Tools in Swift.

Last week, AWS announced three new AWS Heroes. The AWS Heroes program recognizes a vibrant, worldwide group of AWS experts whose enthusiasm for knowledge-sharing has a real impact within the community. Welcome to the community, Dimple, Rola, and Vivek.

We also opened the GenAI Loft in Tel Aviv, Israel. AWS Gen AI Lofts are collaborative spaces and immersive experiences for startups and developers. The Loft content is tailored to address local customer needs – from startups and enterprises to public sector organizations, bringing together developers, investors, and industry experts under one roof.

The loft is open in Tel Aviv until Wednesday, November 19. If you’re in the area, check the list of sessions, workshops, and hackathons today.

If you are a serverless developer, last week was really rich with news. Let’s start with these.

Last week’s launches

Here are the launches that got my attention this week:

• AWS Lambda officially supports Rust , AWS Lambda supports Java 25, and AWS Lambda adds an experimental runtime interface client for Swift – What a busy time for the Lambda service team! The support for the Rust programming language is now generally available. Although the runtime interface client existed for years, it has just been graduated to version 1.0.0. My colleagues Julian and Darko wrote a blog post to showcase the benefits of using Rust for your Lambda functions. Java 25 also has changes that make Lambda functions written in Java more efficient. My colleague Lefteris wrote a blog post to describe these benefits.
• AWS Lambda announces Provisioned Mode for SQS event source mapping – This provides you with the benefits to optimize throughput and handle traffic spikes by provisioning event polling resources.
• Amazon EventBridge introduces enhanced visual rule builder – The Amazon EventBridge enhanced visual rule builder simplifies event-driven application development with an intuitive interface, comprehensive event catalog, and integration with the EventBridge Schema Registry.
• AWS Service Reference Information now supports SDK Operation to Action mapping – Besides the serverless news, this is the biggest announcement of the week in my opinion. The service reference information now includes which operations are supported by AWS services and which IAM permissions are needed to call a given operation. This will help you answer questions such as “I want to call a specific AWS service operation, which IAM permissions do I need?” You can automate the retrieval of service reference information through a simple JSON based API.
• AWS Network Load Balancer (NLB) now supports QUIC protocol in passthrough mode – This provides ultra-low latency traffic forwarding with session stickiness using QUIC Connection IDs. This capability reduces application latency by 25-30% for mobile-first applications through minimized handshakes and connection resilience. NLB operates in passthrough mode, forwarding QUIC traffic directly to targets while maintaining customer control over TLS certificates and end-to-end encryption. The blog post has the details.
• Application Load Balancer (ALB) support client credential flow with JWT verification – This one is important for API developers too. This simplifies the deployment of secure machine-to-machine (M2M) and service-to-service (S2S) communications. This provides ALB with the ability to verify JWTs, reducing architectural complexity and simplifying security implementation.
• AWS KMS now supports Edwards-curve Digital Signature Algorithm (EdDSA) – This capability provides 128-bit security equivalent to NIST P-256 with faster signing performance and compact sizes (64-byte signatures, 32-byte public keys). Ed25519 is ideal for IoT devices and blockchain applications requiring small key and signature sizes.
• Amazon DCV now supports Amazon EC2 Mac instances – This provides high-performance remote desktop access with 4K resolution and 60 FPS performance. You can connect from Windows, Linux, macOS, or web clients with features including time zone redirection and audio output.
• Amazon Linux 2023 version 2025110 is released – It now includes packages for Mountpoint for Amazon S3, the Swift 6.2.1 toolchain, and Node.js 24. Installing Swift on Amazon Linux 2023 virtual machines or containers is now as easy as sudo dnf install -y swiftlang.

Additional updates

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

• Amazon Elastic Kubernetes Service gets independent affirmation of its zero operator access design – Amazon EKS offers a zero operator access posture. AWS personnel cannot access your content. This is achieved through a combination of AWS Nitro System-based instances, restricted administrative APIs, and end-to-end encryption. An independent review by NCC Group confirmed the effectiveness of these security measures.
• Make your web apps hands-free with Amazon Nova Sonic – Amazon Nova Sonic, a foundation model from AAmazon Bedrock, provides you with the ability to create natural, low-latency, bidirectional speech conversations for applications. This provides users with the ability to collaborate with applications through voice and embedded intelligence, unlocking new interaction patterns and enhancing usability. This blog post demonstrates a reference app, Smart Todo App. It shows how voice can be integrated to provide a hands-free experience for task management.
• AWS X-Ray SDKs & Daemon migration to OpenTelemetry – AWS X-Ray is transitioning to OpenTelemetry as its primary instrumentation standard for application tracing. OpenTelemetry-based instrumentation solutions are recommended for producing traces from applications and sending them to AWS X-Ray. X-Ray’s existing console experience and functionality continue to be fully supported and remains unchanged by this transition.
• Powering the world’s largest events: How Amazon CloudFront delivers at scale – Amazon CloudFront achieved a record-breaking peak of 268 terabits per second on November 1, 2025, during major game delivery workloads—enough bandwidth to simultaneously stream live sports in HD to approximately 45 million concurrent viewers. This milestone demonstrates the CloudFront massive scale, powered by 750+ edge locations across 440+ cities globally and 1,140+ embedded PoPs within 100+ ISPs, with the latest generation delivering 3x the performance of previous versions.

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 your 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. I will deliver the opening keynote at the last Community Day of the year in Kinshasa, Democratic Republic of Congo (November 22). The next Community Day will be in Timişoara, România (April 2026). The call for papers is now open.
• 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!

— seb

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: AWS Lambda for Rust, NLB for QUIC protocol, Amazon DCV for Mac, and more (November 17, 2025) 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.

Imagine building AI applications that deliver accurate, current information without the complexity of developing intricate data retrieval systems. Today, we’re excited to announce the general availability of Web Grounding, a new built-in tool for Nova models on Amazon Bedrock.

Web Grounding provides developers with a turnkey Retrieval Augmented Generation (RAG) option that allows the Amazon Nova foundation models to intelligently decide when to retrieve and incorporate relevant up-to-date information based on the context of the prompt. This helps to ground the model output by incorporating cited public sources as context, aiming to reduce hallucinations and improve accuracy.

When should developers use Web Grounding?

Developers should consider using Web Grounding when building applications that require access to current, factual information or need to provide well-cited responses. The capability is particularly valuable across a range of applications, from knowledge-based chat assistants providing up-to-date information about products and services, to content generation tools requiring fact-checking and source verification. It’s also ideal for research assistants that need to synthesize information from multiple current sources, as well as customer support applications where accuracy and verifiability are crucial.

Web Grounding is especially useful when you need to reduce hallucinations in your AI applications or when your use case requires transparent source attribution. Because it automatically handles the retrieval and integration of information, it’s an efficient solution for developers who want to focus on building their applications rather than managing complex RAG implementations.

Getting started
Web Grounding seamlessly integrates with supported Amazon Nova models to handle information retrieval and processing during inference. This eliminates the need to build and maintain complex RAG pipelines, while also providing source attributions that verify the origin of information.

Let’s see an example of asking a question to Nova Premier using Python to call the Amazon Bedrock Converse API with Web Grounding enabled.

First, I created an Amazon Bedrock client using AWS SDK for Python (Boto3) in the usual way. For good practice, I’m using a session, which helps to group configurations and make them reusable. I then create a BedrockRuntimeClient.

try:
    session = boto3.Session(region_name='us-east-1')
    client = session.client(
        'bedrock-runtime')

I then prepare the Amazon Bedrock Converse API payload. It includes a “role” parameter set to “user”, indicating that the message comes from our application’s user (compared to “assistant” for AI-generated responses).

For this demo, I chose the question “What are the current AWS Regions and their locations?” This was selected intentionally because it requires current information, making it useful to demonstrate how Amazon Nova can automatically invoke searches using Web Grounding when it determines that up-to-date knowledge is needed.

# Prepare the conversation in the format expected by Bedrock
question = "What are the current AWS regions and their locations?"
conversation = [
   {
     "role": "user",  # Indicates this message is from the user
     "content": [{"text": question}],  # The actual question text
      }
    ]

First, let’s see what the output is without Web Grounding. I make a call to Amazon Bedrock Converse API.

# Make the API call to Bedrock 
model_id = "us.amazon.nova-premier-v1:0" 
response = client.converse( 
    modelId=model_id, # Which AI model to use 
    messages=conversation, # The conversation history (just our question in this case) 
    )
print(response['output']['message']['content'][0]['text'])

I get a list of all the current AWS Regions and their locations.

Now let’s use Web Grounding. I make a similar call to the Amazon Bedrock Converse API, but declare nova_grounding as one of the tools available to the model.

model_id = "us.amazon.nova-premier-v1:0" 
response = client.converse( 
    modelId=model_id, 
    messages=conversation, 
    toolConfig= {
          "tools":[ 
              {
                "systemTool": {
                   "name": "nova_grounding" # Enables the model to search real-time information
                 }
              }
          ]
     }
)

After processing the response, I can see that the model used Web Grounding to access up-to-date information. The output includes reasoning traces that I can use to follow its thought process and see where it automatically queried external sources. The content of the responses from these external calls appear as [HIDDEN] – a standard practice in AI systems that both protects sensitive information and helps manage output size.

Additionally, the output also includes citationsContent objects containing information about the sources queried by Web Grounding.

Finally, I can see the list of AWS Regions. It finishes with a message right at the end stating that “These are the most current and active AWS regions globally.”

Web Grounding represents a significant step forward in making AI applications more reliable and current with minimum effort. Whether you’re building customer service chat assistants that need to provide up-to-date accurate information, developing research applications that analyze and synthesize information from multiple sources, or creating travel applications that deliver the latest details about destinations and accommodations, Web Grounding can help you deliver more accurate and relevant responses to your users with a convenient turnkey solution that is straightforward to configure and use.

Things to know
Amazon Nova Web Grounding is available today in US East (N. Virginia). Web Grounding will also soon launch on US East (Ohio), and US West (Oregon).

Web Grounding incurs additional cost. Refer to the Amazon Bedrock pricing page for more details.

Currently, you can only use Web Grounding with Nova Premier but support for other Nova models will be added soon.

If you haven’t used Amazon Nova before or are looking to go deeper, try this self-paced online workshop where you can learn how to effectively use Amazon Nova foundation models and related features for text, image, and video processing through hands-on exercises.

Matheus Guimaraes | @codingmatheus

The post Build more accurate AI applications with Amazon Nova Web Grounding 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.