Last week, AWS contributors joined the OpenSearch and Valkey communities at Open Source Summit Korea 2026 and MCP DevSummit Seoul 2026 to meet open source developers and contributors. At the four-day event, community leaders and users of these Linux Foundation open source projects gathered to share knowledge, collaborate on solutions, and push the projects forward.

Leaders of the Korean OpenSearch communities volunteered to participate in the booth, and also had time to network and interact in the user group meetup.

OpenSearch is an open source, enterprise-grade search and observability suite that brings order to unstructured data at scale. On June 9, 2026, OpenSearch 3.7 introduced new tools designed to query, alert, and track SLOs across logs, traces, and metrics through a single interface and retrieve vectors up to 5.5x faster for improved search performance. Since July 30, 2026, you can run OpenSearch version 3.7 on Amazon OpenSearch Service for improvements in vector search performance, search relevance, and Query Insights.

Valkey is an open source high-performance key/value datastore that supports a variety of workloads such as caching, message queues, and it can act as a primary database. On May 19, 2026, Valkey 9.1 introduced a redesigned I/O threading model that improves throughput by up to 17% and reduces memory usage for strings under 128 bytes by up to 20%. Since June 23, 2026, you can run Valkey 9.1 in Amazon ElastiCache for node-based clusters, delivering higher throughput, improved memory efficiency, and stronger access control for multi-tenant workloads.

You can connect with AWS contributors at upcoming OpenSearch and Valkey community events.

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

• Amazon EC2 application status checks: Amazon EC2 introduces a new status check that helps you detect and respond to application-level issues on your EC2 instances. With application status checks, EC2 monitors applications to detect issues such as a web server that has stopped accepting requests, a Docker daemon that is not running, an incorrect networking configuration, or a network interface that is no longer passing traffic. To learn more, visit the Application status checks documentation.
• AWS IAM role manager to set up IAM roles automatically: You can use a new role manager that automatically sets up the IAM roles your AWS services need. When you set up a supported service in the console, role manager creates a default role on your behalf, or reuses one that already exists in your account if it matches the required permissions. Role manager supports six AWS service consoles at launch. To learn more, read How AWS IAM role manager rethinks the starting point for IAM roles.
• OpenAI Daybreak available to eligible customers on Amazon Bedrock: Daybreak is the cyber defense initiative from OpenAI that gives defenders governed access to frontier AI for cybersecurity work. For most security teams, Daybreak Blue, powered by GPT-5.6 Sol, serves as the starting point across defensive workflows including vulnerability discovery, detection engineering, and incident response. Daybreak Red, powered by a new GPT-5.6 Cyber, is designed for advanced, authorized tasks such as vulnerability research, exploit reproduction, and mitigation development. To enroll, contact OpenAI or reach out to your AWS account team for guidance on eligibility. To learn more, read the AI Blog post.
• New foundation models in Amazon SageMaker JumpStart: We’re expanding the portfolio of foundation models available to AWS customers. These models address different enterprise AI challenges with specialized capabilities:

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

Other AWS news
Here are some additional projects and news items you may find interesting:

• The deprecation of email validation in AWS Certificate Manager: ACM will discontinue support for email-validated public certificates by September 30, 2027. If you use email validation for your ACM public certificates, you need to migrate to DNS validation before that date. For Amazon CloudFront distributions, HTTP validation is also available.
• The next-generation AWS VPN Client with CLI support and admin controls: You can use a new AWS VPN Client built on OpenVPN3. With the new client, you get full backward compatibility with existing AWS Client VPN endpoints while delivering the automation capabilities and security posture that enterprise networking teams have been asking for.
• Oracle Exadata on Exascale for Oracle AI Database@AWS: ExaDB-XS brings Exadata-class performance and availability through a consumption-based model. With ExaDB-XS, you can scale compute and storage independently in small increments and pay only for what you consume.

For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.

Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events including AWS Summits and AWS Community Days. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

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

— Channy

The post AWS Weekly Roundup: EC2 application status checks, IAM role manager, OpenAI Daybreak on Bedrock, and more (August 17, 2026) appeared first on The NOKIA Blog.

AWS is committed to bringing you the most advanced foundation models (FMs) in the industry, continuously expanding our selection to include groundbreaking models from leading AI innovators so that you always have access to the latest advancements to drive your business forward.

Today, I am happy to announce the availability of two new OpenAI models with open weights in Amazon Bedrock and Amazon SageMaker JumpStart. OpenAI gpt-oss-120b and gpt-oss-20b models are designed for text generation and reasoning tasks, offering developers and organizations new options to build AI applications with complete control over their infrastructure and data.

These open weight models excel at coding, scientific analysis, and mathematical reasoning, with performance comparable to leading alternatives. Both models support a 128K context window and provide adjustable reasoning levels (low/medium/high) to match your specific use case requirements. The models support external tools to enhance their capabilities and can be used in an agentic workflow, for example, using a framework like Strands Agents.

With Amazon Bedrock and Amazon SageMaker JumpStart, AWS gives you the freedom to innovate with access to hundreds of FMs from leading AI companies, including OpenAI open weight models. With our comprehensive selection of models, you can match your AI workloads to the perfect model every time.

Through Amazon Bedrock, you can seamlessly experiment with different models, mix and match capabilities, and switch between providers without rewriting code—turning model choice into a strategic advantage that helps you continuously evolve your AI strategy as new innovations emerge. These new models are available in Bedrock via an OpenAI-compatible endpoint. You can point the OpenAI SDK to this endpoint or use the Bedrock InvokeModel and Converse API.

With SageMaker JumpStart, you can quickly evaluate, compare, and customize models for your use case. You can then deploy the original or the customized model in production with the SageMaker AI console or using the SageMaker Python SDK.

Let’s see how these work in practice.

Getting started with OpenAI open weight models in Amazon Bedrock
In the Amazon Bedrock console, I choose Model access from the Configure and learn section of the navigation pane. Then, I navigate to the two listed OpenAI models on this page and request access.

Now that I have access, I use the Chat/Test playground to test and evaluate the models. I select OpenAI as the category and then the gpt-oss-120b model.

Using this model, I run the following sample prompt:

A family has $5,000 to save for their vacation next year. They can place the money in a savings account earning 2% interest annually or in a certificate of deposit earning 4% interest annually but with no access to the funds until the vacation. If they need $1,000 for emergency expenses during the year, how should they divide their money between the two options to maximize their vacation fund?

This prompt generates an output that includes the chain of thought used to produce the result.

I can use these models with the OpenAI SDK by configuring the API endpoint (base URL) and using an Amazon Bedrock API key for authentication. For example, I set this environment variables to use the US West (Oregon) AWS Region endpoint (us-west-2) and my Amazon Bedrock API key:

export OPENAI_API_KEY="<my-bedrock-api-key>"
export OPENAI_BASE_URL="https://bedrock-runtime.us-west-2.amazonaws.com/openai/v1"

Now I invoke the model using the OpenAI Python SDK.

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create( 
    messages=[{ "role": "user", "content": "Tell me the square root of 42 ^ 3" }],
    model="openai.gpt-oss-120b-1:0",
    stream=False
)

for item in response:
    print(item)

I save the code (test-openai.py file), install the dependencies, and run the agent locally:

pip install openai
python test-openai.py

To build an AI agent, I can choose any framework that supports the Amazon Bedrock API or the OpenAI API. For example, here’s the starting code for Strands Agents using the Amazon Bedrock API:

from strands import Agent
from strands.models import BedrockModel

bedrock_model = BedrockModel(
    model_id="openai.gpt-oss-120b-1:0",
    region_name="us-west-2",
    streaming=False
)

agent = Agent(
    model=bedrock_model
)

agent("Tell me the square root of 42 ^ 3")

I save the code (test-strands.py file), install the dependencies, and run the agent locally:

pip install strands-agents
python test-strands.py

When I am satisfied with the agent, I can deploy in production using the capabilities offered by Amazon Bedrock AgentCore, including a fully managed serverless runtime and memory and identity management.

Getting started with OpenAI open weight models in Amazon SageMaker JumpStart
In the Amazon SageMaker AI console, you can use OpenAI open weight models in the SageMaker Studio. The first time I do this, I need to set up a SageMaker domain. There are options to set it up for a single user (simpler) or an organization. For these tests, I use a single user setup.

In the SageMaker JumpStart model view, I have access to a detailed description of the gpt-oss-120b or gpt-oss-20b model.

I choose the gpt-oss-20b model and then deploy the model. In the next steps, I select the instance type and the initial instance count. After a few minutes, the deployment creates an endpoint that I can then invoke in SageMaker Studio and using any AWS SDKs.

To learn more, visit GPT OSS models from OpenAI are now available on SageMaker JumpStart in the AWS Artificial Intelligence Blog.

Things to know
The new OpenAI open weight models are now available in Amazon Bedrock in the US West (Oregon) AWS Region, while Amazon SageMaker JumpStart supports these models in US East (Ohio, N. Virginia) and Asia Pacific (Mumbai, Tokyo).

Each model comes equipped with full chain-of-thought output capabilities, providing you with detailed visibility into the model’s reasoning process. This transparency is particularly valuable for applications requiring high levels of interpretability and validation. These models give you the freedom to modify, adapt, and customize them to your specific needs. This flexibility allows you to fine-tune the models for your unique use cases, integrate them into your existing workflows, and even build upon them to create new, specialized models tailored to your industry or application.

Security and safety are built into the core of these models, with comprehensive evaluation processes and safety measures in place. The models maintain compatibility with the standard GPT-4 tokenizer.

Both models can be used in your preferred environment, whether that’s through the serverless experience of Amazon Bedrock or the extensive machine learning (ML) development capabilities of SageMaker JumpStart. For information about the costs associated with using these models and services, visit the Amazon Bedrock pricing and Amazon SageMaker AI pricing pages.

To learn more, see the parameters for the models and the chat completions API in the Amazon Bedrock documentation.

Get started today with OpenAI open weight models on AWS in the Amazon Bedrock console or in Amazon SageMaker AI console.

– Danilo

The post OpenAI open weight models now available on AWS appeared first on The NOKIA Blog.

Every Monday we tell you about the best releases and blogs that caught our attention last week. This week I’m making an exception to include a release from today: Amazon Bedrock API keys. This new feature simplifies generative AI development by providing direct API authentication without needing to manually configure IAM principals and policies.
Amazon Bedrock API keys support both long-term (with flexible validity duration) and short-term (up to 12 hours or console session duration) options. These improvements reduce developer onboarding time while maintaining enterprise security controls. To learn more about API keys in Amazon Bedrock, visit the API keys documentation.

Before continuing with this AWS Weekly Roundup, I’d like to share that last month I moved with my family to San Francisco, California, to start a new role as Developer Advocate/SDE, GenAI.

This excites me because I’ll have the opportunity to connect with new communities in the Bay Area while tackling exciting new challenges. If you’re part of a community focused on building generative AI and agentics applications, or know of one, I’d love to connect. Let’s connect!

Last week’s launches
Here are the launches from last week:

• New Amazon EC2 C8gn instances powered by AWS Graviton4 offering up to 600Gbps network bandwidth – Amazon Elastic Compute Cloud (Amazon EC2) C8gn instances are now generally available, powered by AWS Graviton4 processors and 6th generation AWS Nitro Cards. These network-optimized instances deliver up to 600 Gbps network bandwidth. This represents the highest bandwidth among EC2 network-optimized instances, with up to 192 vCPUs and 384 GiB memory. They provide 30% higher compute performance than C7gn instances and are ideal for network-intensive workloads like virtual appliances, data analytics, and cluster computing jobs.
• Build the highest resilience apps with multi-Region strong consistency in Amazon DynamoDB global tables – Amazon DynamoDB global tables now supports multi-Region strong consistency (MRSC) for applications requiring zero Recovery Point Objective (RPO). This capability ensures applications can read the latest data from any Region during outages, addressing critical needs in payment processing and financial services. MRSC requires three AWS Regions configured as either three full replicas or two replicas plus a witness, providing the highest level of application resilience for mission-critical workloads.
• Amazon Nova Canvas update: Virtual try-on and style options now available – Amazon Nova Canvas introduces virtual try-on capabilities that help you visualize how clothing looks on a person by combining two images, plus eight new pre-trained style options (3D animation, design sketch, vector illustration, graphic novel, etc.) for generating images with improved artistic consistency. Available in three AWS Regions, these features enhance AI-powered image generation capabilities for retailers and content creators seeking realistic product visualizations.
• Amazon Q in Connect now supports 7 languages for proactive recommendations – Amazon Q in Connect, a generative AI-powered assistant for customer service, now provides proactive recommendations in seven languages: English, Spanish, French, Portuguese, Mandarin, Japanese, and Korean. The AI-powered customer service assistant detects customer intent during voice and chat interactions to help agents resolve issues quickly and accurately.
• Amazon Aurora MySQL and Amazon RDS for MySQL integration with Amazon SageMaker is now available – This integration provides near real-time data availability for analytics. It automatically extracts MySQL data into lakehouses with Apache Iceberg compatibility. You can then access this data seamlessly through various analytics engines and machine learning tools.
• Amazon Aurora DSQL is now available in additional AWS Regions – Amazon Aurora DSQL expands to Asia Pacific (Seoul) and now supports multi-Region clusters across Asia Pacific and European regions. This serverless, distributed SQL database offers unlimited scalability, highest availability, and zero infrastructure management with AWS Free Tier access.

Other AWS blog posts

• Optimize RAG in production environments using Amazon SageMaker JumpStart and Amazon OpenSearch Service – Learn how to optimize Retrieval Augmented Generation (RAG) in production environments using Amazon SageMaker JumpStart and Amazon OpenSearch Service. This comprehensive guide demonstrates implementing RAG workflows with LangChain, covers OpenSearch optimization strategies, provides setup instructions, and explains benefits of combining these AWS services for scalable, cost-effective generative AI applications.v
• Agentic GenAI App Using Bedrock, MCP servers on EKS – This post shows how to build a scalable AI chat application using Amazon Bedrock, Strands Agent, and Model Context Protocol (MCP) servers deployed on Amazon Elastic Kubernetes Service (Amazon EKS). The architecture combines agentic workflows with containerized microservices for intelligent, auto-scaling conversations with multiple foundation models.
• Enforce table level access control on data lake tables using AWS Glue 5.0 with AWS Lake Formation – AWS Glue 5.0 introduces Full-Table Access (FTA) control for Apache Spark with AWS Lake Formation, providing table-level security without fine-grained access overhead. This feature supports native Spark SQL/DataFrames for Lake Formation tables. It enables read/write operations on Iceberg and Hive tables with improved performance and lower costs.

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

• AWS re:Invent – Register now to get a head start on choosing your best learning path, booking travel and accommodations, and bringing your team to learn, connect, and have fun. Early-career professionals can apply for the All Builders Welcome Grant program, designed to remove financial barriers and create diverse pathways into cloud technology. Applications are now open and close on July 15, 2025.
• AWS NY Summit – You can gain insights from Swami’s keynote featuring the latest cutting-edge AWS technologies in compute, storage, and generative AI. My News Blog team is also preparing some exciting news for you. If you’re unable to attend in person, you can still participate by registering for the global live stream. Also, save the date for these upcoming Summits in July and August near your city.
• AWS Builders Online Series – If you’re based in one of the Asia Pacific time zones, join and learn fundamental AWS concepts, architectural best practices, and hands-on demonstrations to help you build, migrate, and deploy your workloads on AWS.
• Join AWS Gen AI Lofts – Experience AWS Gen AI Lofts across San Francisco, Berlin, Dubai, Dublin, Bengaluru, Manchester, Paris, Tel Aviv, and additional locations – hands-on workshops, expert guidance, investor networking, and collaborative spaces designed to accelerate your generative AI startup journey.

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

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

— Eli

The post AWS Weekly Roundup: Amazon Bedrock API keys, EC2 C8gn instances, Amazon Nova Canvas virtual try-on, and more (July 7, 2025) appeared first on The NOKIA Blog.

The newest AI models from Meta, Llama 4 Scout 17B and Llama 4 Maverick 17B, are now available as a fully managed, serverless option in Amazon Bedrock. These new foundation models (FMs) deliver natively multimodal capabilities with early fusion technology that you can use for precise image grounding and extended context processing in your applications.

Llama 4 uses an innovative mixture-of-experts (MoE) architecture that provides enhanced performance across reasoning and image understanding tasks while optimizing for both cost and speed. This architectural approach enables Llama 4 to offer improved performance at lower cost compared to Llama 3, with expanded language support for global applications.

The models were already available on Amazon SageMaker JumpStart, and you can now use them in Amazon Bedrock to streamline building and scaling generative AI applications with enterprise-grade security and privacy.

Llama 4 Maverick 17B – A natively multimodal model featuring 128 experts and 400 billion total parameters. It excels in image and text understanding, making it suitable for versatile assistant and chat applications. The model supports a 1 million token context window, giving you the flexibility to process lengthy documents and complex inputs.

Llama 4 Scout 17B – A general-purpose multimodal model with 16 experts, 17 billion active parameters, and 109 billion total parameters that delivers superior performance compared to all previous Llama models. Amazon Bedrock currently supports a 3.5 million token context window for Llama 4 Scout, with plans to expand in the near future.

Use cases for Llama 4 models
You can use the advanced capabilities of Llama 4 models for a wide range of use cases across industries:

Enterprise applications – Build intelligent agents that can reason across tools and workflows, process multimodal inputs, and deliver high-quality responses for business applications.

Multilingual assistants – Create chat applications that understand images and provide high-quality responses across multiple languages, making them accessible to global audiences.

Code and document intelligence – Develop applications that can understand code, extract structured data from documents, and provide insightful analysis across large volumes of text and code.

Customer support – Enhance support systems with image analysis capabilities, enabling more effective problem resolution when customers share screenshots or photos.

Content creation – Generate creative content across multiple languages, with the ability to understand and respond to visual inputs.

Research – Build research applications that can integrate and analyze multimodal data, providing insights across text and images.

Using Llama 4 models in Amazon Bedrock
To use these new serverless models in Amazon Bedrock, I first need to request access. In the Amazon Bedrock console, I choose Model access from the navigation pane to toggle access to Llama 4 Maverick 17B and Llama 4 Scout 17B models.

The Llama 4 models can be easily integrated into your applications using the Amazon Bedrock Converse API, which provides a unified interface for conversational AI interactions.

Here’s an example of how to use the AWS SDK for Python (Boto3) with Llama 4 Maverick for a multimodal conversation:

import boto3
import json
import os

AWS_REGION = "us-west-2"
MODEL_ID = "us.meta.llama4-maverick-17b-instruct-v1:0"
IMAGE_PATH = "image.jpg"


def get_file_extension(filename: str) -> str:
    """Get the file extension."""
    extension = os.path.splitext(filename)[1].lower()[1:] or 'txt'
    if extension == 'jpg':
        extension = 'jpeg'
    return extension


def read_file(file_path: str) -> bytes:
    """Read a file in binary mode."""
    try:
        with open(file_path, 'rb') as file:
            return file.read()
    except Exception as e:
        raise Exception(f"Error reading file {file_path}: {str(e)}")

bedrock_runtime = boto3.client(
    service_name="bedrock-runtime",
    region_name=AWS_REGION
)

request_body = {
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "text": "What can you tell me about this image?"
                },
                {
                    "image": {
                        "format": get_file_extension(IMAGE_PATH),
                        "source": {"bytes": read_file(IMAGE_PATH)},
                    }
                },
            ],
        }
    ]
}

response = bedrock_runtime.converse(
    modelId=MODEL_ID,
    messages=request_body["messages"]
)

print(response["output"]["message"]["content"][-1]["text"])

This example demonstrates how to send both text and image inputs to the model and receive a conversational response. The Converse API abstracts away the complexity of working with different model input formats, providing a consistent interface across models in Amazon Bedrock.

For more interactive use cases, you can also use the streaming capabilities of the Converse API:

response_stream = bedrock_runtime.converse_stream(
    modelId=MODEL_ID,
    messages=request_body['messages']
)

stream = response_stream.get('stream')
if stream:
    for event in stream:

        if 'messageStart' in event:
            print(f"nRole: {event['messageStart']['role']}")

        if 'contentBlockDelta' in event:
            print(event['contentBlockDelta']['delta']['text'], end="")

        if 'messageStop' in event:
            print(f"nStop reason: {event['messageStop']['stopReason']}")

        if 'metadata' in event:
            metadata = event['metadata']
            if 'usage' in metadata:
                print(f"Usage: {json.dumps(metadata['usage'], indent=4)}")
            if 'metrics' in metadata:
                print(f"Metrics: {json.dumps(metadata['metrics'], indent=4)}")

With streaming, your applications can provide a more responsive experience by displaying model outputs as they are generated.

Things to know
The Llama 4 models are available today with a fully managed, serverless experience in Amazon Bedrock in the US East (N. Virginia) and US West (Oregon) AWS Regions. You can also access Llama 4 in US East (Ohio) via cross-region inference.

As usual with Amazon Bedrock, you pay for what you use. For more information, see Amazon Bedrock pricing.

These models support 12 languages for text (English, French, German, Hindi, Italian, Portuguese, Spanish, Thai, Arabic, Indonesian, Tagalog, and Vietnamese) and English when processing images.

To start using these new models today, visit the Meta Llama models section in the Amazon Bedrock User Guide. You can also explore how our Builder communities are using Amazon Bedrock in their solutions in the generative AI section of our community.aws site.

— Danilo

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The post Llama 4 models from Meta now available in Amazon Bedrock serverless appeared first on The NOKIA Blog.

Join us for the AWS Developer Day on February 20! This virtual event is designed to help developers and teams incorporate cutting-edge yet responsible generative AI across their development lifecycle to accelerate innovation.

In his keynote, Jeff Barr, Vice President of AWS Evangelism, shares his thoughts on the next generation of software development based on generative AI, the skills needed to thrive in this changing environment, and how he sees it evolving in the future.

Get a first look at exciting technical deep-dive and product updates about Amazon Q Developer, AWS Amplify, and GitLab Duo with Amazon Q. You get the chance to explore real-world use cases, live coding demos, interactive sessions, and community spotlight sessions with Christian Bonzelet (AWS Community Builder), Hazel Saenz (AWS Serverless Hero), Matt Lewis (AWS Data Hero), and Johannes Koch (AWS DevTools Hero). Please sign up for this event now!

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

Updating AWS SDK defaults for AWS STS – As we shared upcoming changes to the AWS Security Token Service (AWS STS) global endpoint to improve the resiliency and performance of your applications, we’re updating two defaults of AWS Software Development Kits (AWS SDKs) and AWS Command Line Interfaces (AWS CLIs) on July 31st 2025 – the default AWS STS service to regional, and the default retry strategy to standard. We recommend that you test your application before the release to avoid an unexpected experience after updating.

Introducing the AWS Trust Center – Chris Betz, CISO at Amazon Web Services (AWS), shared AWS Trust Center, a new online resource communicating how we approach securing your assets in the cloud. This resource is a window into our security practices, compliance programs, and data protection controls that demonstrates how we work to earn your trust every day.

AWS CloudTrail network activity events for VPC endpoint – This feature provides you with a powerful tool to enhance your security posture, detect potential threats, and gain deeper insights into your VPC network traffic. This feature addresses your critical needs for comprehensive visibility and control over your AWS environments.

AWS Verified Access support for non-HTTP resources – AWS Verified Access now extends beyond HTTP apps to provide VPN-less, secure access to non-HTTP resources like Amazon Relational Database Service (Amazon RDS) databases, enabling improved security and enhanced user experience for both web applications and database connections. To learn more, visit the Verified Access endpoints page and a video tutorial.

New subnet management of Network Load Balancer (NLB) – NLBs were previously restricted to only adding subnets in new Availability Zones, and they now support full subnet management, including removal of subnets, matching the capabilities of Application Load Balancer (ALB). This enhancement offers organizations greater control over their network architecture and brings consistency to AWS load balancing services.

Meta SAM 2.1 and Falcon 3 models in Amazon SageMaker JumpStart – You can use Meta’s Segment Anything Model (SAM) 2.1 with state-of-the-art video and image segmentation capabilities in a single model. You can also use the Falcon 3 family with five models ranging from 1 to 10 billion parameters, with a focus on enhancing science, math, and coding capabilities. To learn more, visit SageMaker JumpStart pretrained models and Getting started with Amazon SageMaker JumpStart.

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

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

AWS Documentation update – Greg Wilson, a lead of AWS Documentation, SDK, and CLI teams shared an insightful blog post about the progress, challenges, and what’s next for technical documentation for 200+ AWS services. It includes AWS Decision Guides for choosing the right service for specific needs; optimizing documents for readability, such as doubled code samples; and improving usability, such as dark mode and auto-suggest with top global navigation controls. You can also learn about how we use generative AI to help create technical documents.

AWS Well-Architected for Enterprises – This is a new free digital course designed for technical professionals who architect, build, and operate AWS solutions at scale. This intermediate-level course will help you optimize your cloud architecture while aligning to your business goals. The course takes approximately 1 hour to complete and includes a knowledge check at the end to reinforce your learning.

Integrating AWS with .NET Aspire – The .NET team at AWS has been working on integrations for connecting your .NET applications to AWS resources. Learn about how to automatically deploy AWS application resources using Aspire.Hosting.AWS NuGet package for NET Aspire, an open source framework building cloud-ready applications.

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

AWS Innovate: Generative AI + Data – Join a free online conference focusing on generative AI and data innovations. Available in multiple geographic regions: APJC and EMEA (March 6), North America (March 13), Greater China Region (March 14), and Latin America (April 8).

AWS Summits – Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Paris (April 9), Amsterdam (April 16), London (April 30), and Poland (May 5).

AWS GenAI Lofts – GenAI Lofts offer collaborative spaces and immersive experiences for startups and developers. You can join in-person GenAI Loft San Francisco events such as Built on Amazon Bedrock demo nights (February 19), SageMaker Unified Studio Demo for Startups (February 21), and Hands-on with Agentic Graph RAG Workshop (February 25). GenAI Loft Berlin has its Opening Day on February 24 and goes to March 7.

AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Karachi, Pakistan (February 22), Milan, Italy (April 2), Bay Area – Security Edition (April 4), Timișoara, Romania (April 10), and Prague, Czeh Republic (April 29).

AWS re:Inforce – Mark your calendars for AWS re:Inforce (June 16–18) in Philadelphia, PA. AWS re:Inforce is a learning conference focused on AWS security solutions, cloud security, compliance, and identity. You can subscribe for event updates now!

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

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

— Channy

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 Developer Day, Trust Center, Well-Architected for Enterprises, and more (Feb 17, 2025) appeared first on The NOKIA Blog.

During this past AWS re:Invent, Amazon CEO Andy Jassy shared valuable lessons learned from Amazon’s own experience developing nearly 1,000 generative AI applications across the company. Drawing from this extensive scale of AI deployment, Jassy offered three key observations that have shaped Amazon’s approach to enterprise AI implementation.

First is that as you get to scale in generative AI applications, the cost of compute really matters. People are very hungry for better price performance. The second is actually quite difficult to build a really good generative AI application. The third is the diversity of the models being used when we gave our builders freedom to pick what they want to do. It doesn’t surprise us, because we keep learning the same lesson over and over and over again, which is that there is never going to be one tool to rule the world.

As Andy emphasized, a broad and deep range of models provided by Amazon empowers customers to choose the precise capabilities that best serve their unique needs. By closely monitoring both customer needs and technological advancements, AWS regularly expands our curated selection of models to include promising new models alongside established industry favorites. This ongoing expansion of high-performing and differentiated model offerings helps customers stay at the forefront of AI innovation.

This leads us to Chinese AI startup DeepSeek. DeepSeek launched DeepSeek-V3 on December 2024 and subsequently released DeepSeek-R1, DeepSeek-R1-Zero with 671 billion parameters, and DeepSeek-R1-Distill models ranging from 1.5–70 billion parameters on January 20, 2025. They added their vision-based Janus-Pro-7B model on January 27, 2025. The models are publicly available and are reportedly 90-95% more affordable and cost-effective than comparable models. Per Deepseek, their model stands out for its reasoning capabilities, achieved through innovative training techniques such as reinforcement learning.

Today, you can now deploy DeepSeek-R1 models in Amazon Bedrock and Amazon SageMaker AI. Amazon Bedrock is best for teams seeking to quickly integrate pre-trained foundation models through APIs. Amazon SageMaker AI is ideal for organizations that want advanced customization, training, and deployment, with access to the underlying infrastructure. Additionally, you can also use AWS Trainium and AWS Inferentia to deploy DeepSeek-R1-Distill models cost-effectively via Amazon Elastic Compute Cloud (Amazon EC2) or Amazon SageMaker AI.

With AWS, you can use DeepSeek-R1 models to build, experiment, and responsibly scale your generative AI ideas by using this powerful, cost-efficient model with minimal infrastructure investment. You can also confidently drive generative AI innovation by building on AWS services that are uniquely designed for security. We highly recommend integrating your deployments of the DeepSeek-R1 models with Amazon Bedrock Guardrails to add a layer of protection for your generative AI applications, which can be used by both Amazon Bedrock and Amazon SageMaker AI customers.

You can choose how to deploy DeepSeek-R1 models on AWS today in a few ways: 1/ Amazon Bedrock Marketplace for the DeepSeek-R1 model, 2/ Amazon SageMaker JumpStart for the DeepSeek-R1 model, 3/ Amazon Bedrock Custom Model Import for the DeepSeek-R1-Distill models, and 4/ Amazon EC2 Trn1 instances for the DeepSeek-R1-Distill models.

Let me walk you through the various paths for getting started with DeepSeek-R1 models on AWS. Whether you’re building your first AI application or scaling existing solutions, these methods provide flexible starting points based on your team’s expertise and requirements.

1. The DeepSeek-R1 model in Amazon Bedrock Marketplace
Amazon Bedrock Marketplace offers over 100 popular, emerging, and specialized FMs alongside the current selection of industry-leading models in Amazon Bedrock. You can easily discover models in a single catalog, subscribe to the model, and then deploy the model on managed endpoints.

To access the DeepSeek-R1 model in Amazon Bedrock Marketplace, go to the Amazon Bedrock console and select Model catalog under the Foundation models section. You can quickly find DeepSeek by searching or filtering by model providers.

After checking out the model detail page including the model’s capabilities, and implementation guidelines, you can directly deploy the model by providing an endpoint name, choosing the number of instances, and selecting an instance type.

You can also configure advanced options that let you customize the security and infrastructure settings for the DeepSeek-R1 model including VPC networking, service role permissions, and encryption settings. For production deployments, you should review these settings to align with your organization’s security and compliance requirements.

With Amazon Bedrock Guardrails, you can independently evaluate user inputs and model outputs. You can control the interaction between users and DeepSeek-R1 with your defined set of policies by filtering undesirable and harmful content in generative AI applications. The DeepSeek-R1 model in Amazon Bedrock Marketplace can only be used with Bedrock’s ApplyGuardrail API to evaluate user inputs and model responses for custom and third-party FMs available outside of Amazon Bedrock. To learn more, read Implement model-independent safety measures with Amazon Bedrock Guardrails.

Amazon Bedrock Guardrails can also be integrated with other Bedrock tools including Amazon Bedrock Agents and Amazon Bedrock Knowledge Bases to build safer and more secure generative AI applications aligned with responsible AI policies. To learn more, visit the AWS Responsible AI page.

When using DeepSeek-R1 model with Bedrock’s InvokeModel API and the Playground Console, please use DeepSeek’s chat template for optimal results. For example, <｜begin▁of▁sentence｜><｜User｜>content for inference<｜Assistant｜>.

Refer to this step-by-step guide on how to deploy the DeepSeek-R1 model in Amazon Bedrock Marketplace. To learn more, visit Deploy models in Amazon Bedrock Marketplace.

2. The DeepSeek-R1 model in Amazon SageMaker JumpStart
Amazon SageMaker JumpStart is a machine learning (ML) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with just a few clicks. To deploy DeepSeek-R1 in SageMaker JumpStart, you can discover the DeepSeek-R1 model in SageMaker Unified Studio, SageMaker Studio, SageMaker AI console, or programmatically through the SageMaker Python SDK.

In the Amazon SageMaker AI console, open SageMaker Unified Studio or SageMaker Studio. In case of SageMaker Studio, choose JumpStart and search for “DeepSeek-R1” in the All public models page.

You can select the model and choose deploy to create an endpoint with default settings. When the endpoint comes InService, you can make inferences by sending requests to its endpoint.

You can derive model performance and ML operations controls with Amazon SageMaker AI features such as Amazon SageMaker Pipelines, Amazon SageMaker Debugger, or container logs. The model is deployed in an AWS secure environment and under your virtual private cloud (VPC) controls, helping to support data security.

As like Bedrock Marketpalce, you can use the ApplyGuardrail API in the SageMaker JumpStart to decouple safeguards for your generative AI applications from the DeepSeek-R1 model. You can now use guardrails without invoking FMs, which opens the door to more integration of standardized and thoroughly tested enterprise safeguards to your application flow regardless of the models used.

Refer to this step-by-step guide on how to deploy DeepSeek-R1 in Amazon SageMaker JumpStart. To learn more, visit Discover SageMaker JumpStart models in SageMaker Unified Studio or Deploy SageMaker JumpStart models in SageMaker Studio.

3. DeepSeek-R1-Distill models using Amazon Bedrock Custom Model Import
Amazon Bedrock Custom Model Import provides the ability to import and use your customized models alongside existing FMs through a single serverless, unified API without the need to manage underlying infrastructure. With Amazon Bedrock Custom Model Import, you can import DeepSeek-R1-Distill Llama models ranging from 1.5–70 billion parameters. As I highlighted in my blog post about Amazon Bedrock Model Distillation, the distillation process involves training smaller, more efficient models to mimic the behavior and reasoning patterns of the larger DeepSeek-R1 model with 671 billion parameters by using it as a teacher model.

After storing these publicly available models in an Amazon Simple Storage Service (Amazon S3) bucket or an Amazon SageMaker Model Registry, go to Imported models under Foundation models in the Amazon Bedrock console and import and deploy them in a fully managed and serverless environment through Amazon Bedrock. This serverless approach eliminates the need for infrastructure management while providing enterprise-grade security and scalability.

Refer to this step-by-step guide on how to deploy DeepSeek-R1 models using Amazon Bedrock Custom Model Import. To learn more, visit Import a customized model into Amazon Bedrock.

4. DeepSeek-R1-Distill models using AWS Trainium and AWS Inferentia
AWS Deep Learning AMIs (DLAMI) provides customized machine images that you can use for deep learning in a variety of Amazon EC2 instances, from a small CPU-only instance to the latest high-powered multi-GPU instances. You can deploy the DeepSeek-R1-Distill models on AWS Trainuim1 or AWS Inferentia2 instances to get the best price-performance.

To get started, go to Amazon EC2 console and launch a trn1.32xlarge EC2 instance with the Neuron Multi Framework DLAMI called Deep Learning AMI Neuron (Ubuntu 22.04).

Once you have connected to your launched ec2 instance, install vLLM, an open-source tool to serve Large Language Models (LLMs) and download the DeepSeek-R1-Distill model from Hugging Face. You can deploy the model using vLLM and invoke the model server.

To learn more, refer to this step-by-step guide on how to deploy DeepSeek-R1-Distill Llama models on AWS Inferentia and Trainium.

You can also visit the DeepSeek-R1-Distill-Llama-8B or deepseek-ai/DeepSeek-R1-Distill-Llama-70B model cards on Hugging Face. Choose Deploy and then Amazon SageMaker. From the AWS Inferentia and Trainium tab, copy the example code for deploy DeepSeek-R1-Distill Llama models.

Since the release of DeepSeek-R1, various guides of its deployment for Amazon EC2 and Amazon Elastic Kubernetes Service (Amazon EKS) have been posted. Here is some additional material for you to check out:

Things to know
Here are a few important things to know.

• Pricing – For publicly available models like DeepSeek-R1, you are charged only the infrastructure price based on inference instance hours you select for Amazon Bedrock Markeplace, Amazon SageMaker JumpStart, and Amazon EC2. For the Bedrock Custom Model Import, you are only charged for model inference, based on the number of copies of your custom model is active, billed in 5-minute windows. To learn more, check out the Amazon Bedrock Pricing, Amazon SageMaker AI Pricing, and Amazon EC2 Pricing pages.
• Data security – You can use enterprise-grade security features in Amazon Bedrock and Amazon SageMaker to help you make your data and applications secure and private. This means your data is not shared with model providers, and is not used to improve the models. This applies to all models—proprietary and publicly available—like DeepSeek-R1 models on Amazon Bedrock and Amazon SageMaker. To learn more, visit Amazon Bedrock Security and Privacy and Security in Amazon SageMaker AI.

Now available
DeepSeek-R1 is generally available today in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. You can also use DeepSeek-R1-Distill models using Amazon Bedrock Custom Model Import and Amazon EC2 instances with AWS Trainum and Inferentia chips.

Give DeepSeek-R1 models a try today in the Amazon Bedrock console, Amazon SageMaker AI console, and Amazon EC2 console, and send feedback to AWS re:Post for Amazon Bedrock and AWS re:Post for SageMaker AI or through your usual AWS Support contacts.

— Channy

The post DeepSeek-R1 models now available on AWS appeared first on The NOKIA Blog.

Happy New Year! We are witnessing technology augment human ingenuity in inspiring ways. In the coming years, using technology for positive impact will redefine the way we think about success. Amazon CTO, Dr. Werner Vogels, offers five forward-looking tech predictions for 2025, and beyond:

• The workforce of tomorrow is mission-driven
• A new era of energy efficiency drives innovation
• Technology tips the scales in the discovery of truth
• Open data drives decentralized disaster preparedness
• Intention-driven consumer technology takes hold

Download the Werner Vogels’ Tech Predictions for 2025 and Beyond ebook, or read Werner’s All Things Distributed blog to learn more about how these technological trends are shaping our world and paving the way for a more innovative, efficient, and purposeful future.

AWS re:Invent 2025 videos and re:Caps
If you’re looking to catch up on re:Invent announcements or delve deeper into the latest AWS innovations, you have several options available:

Launches from the last few weeks
Since our last week in review on December 16, 2024, I’d like to highlight some launches from year end, as well as from last week:

Availability of Llama 3.3 70B in Amazon SageMaker JumpStart and in Amazon Bedrock – Meta’s Llama 3.3 70B represents a significant advancement in model efficiency and performance optimization. Llama 3.3 70B is a text-only instruction-tuned model that provides enhanced performance relative to Llama 3.1 70B and Llama 3.2 90B, when used for text-only applications. You can now use the model in both Amazon SageMaker JumpStart, as well as Amazon Bedrock.

Availability of Stable Diffusion 3.5 Large in Amazon Bedrock – Stable Diffusion 3.5 Large by Stability AI is the most powerful model in the Stable Diffusion family at 8.1 billion parameters trained on Amazon SageMaker HyperPod. You can now use Stable Diffusion 3.5 Large in Amazon Bedrock to generate high-quality images from text descriptions.

New Amazon Kinesis source connector for Apache Flink – The Apache Flink community has released version 5.0.0 of AWS services connectors, an AWS open source contribution. This release introduces the Kinesis Streams Source, a new connector for reading data from Amazon Kinesis Data Streams, replacing the previous Kinesis Consumer.

Support of AWS Global Accelerator in Amazon WorkSpaces Personal – Amazon WorkSpaces Personal now integrates with AWS Global Accelerator (AGA) to enhance WorkSpaces connection performance by optimizing streaming traffic through the AWS Global Network and edge locations. This feature particularly benefits customers whose end users connect to WorkSpaces across long distances. You can enable the AGA feature at either the WorkSpaces directory level or for individual WorkSpaces running Amazon DCV protocol.

New resource insights in AWS Resource Explorer – With the enhanced Resource Explorer experience, relevant data and insights from multiple AWS services is centralized for supported resource types. You can use the new features to take actions on resources directly from the Resource Explorer console, such as manage tags, add resources to applications, and get additional information about a resource with Amazon Q.

General availability of custom billing view in AWS Billing and Cost Management – Custom billing view allows you to provide application and business unit owners access to relevant cost management data across multiple AWS accounts using a single view in AWS Cost Explorer without granting access to the AWS management account. You can create filtered views of cost management data based on cost allocation tags or specific AWS accounts.

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

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

AWS at CES 2025 (January 7-10) – AWS will be representing some of the latest cloud services and solutions that are purpose built for the automotive, mobility, transportation, and manufacturing industries. Join us to learn about the latest cloud capabilities across generative AI, software define vehicles, product engineering, sustainability, new digital customer experiences, connected mobility, autonomous driving, and so much more, in Amazon Experience Area.

AWS at NRF 2025 (January 12-14) – Join AWS at the retail’s big show to see generative AI and cutting-edge technologies in action. Listen to innovative big ideas sessions, curated TechTalks, and experience the latest in retail trends, technologies, and more.

Amazon QuickSight Learning Series – Kick off your 2025 by supercharging your data skills. Join the online Learning Series in January to learn about the cutting-edge features of Amazon QuickSight unveiled at re:Invent 2024.

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

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

–Prasad

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 Happy New Year! AWS Weekly Roundup: 2025 Tech Predictions, Llama 3.3 70B, Stable Diffusion 3.5 Large, custom billing view, and more (January 6, 2025) appeared first on The NOKIA Blog.