If there’s one theme that defined last week, it’s choice. The frontier models keep arriving, and the interesting question is no longer just “how smart is it?” but “which model fits this step, at this cost, at this latency?” That’s exactly what landed on Amazon Bedrock over the past few days: GPT-6 Sol and GPT-6 Luna from OpenAI, giving you two new points on the intelligence-versus-efficiency curve, and Claude Opus 5.5 from Anthropic, the first of the Claude 5.5 family.

GPT-6 Sol is built for the demanding, recurring work of development and operations, while GPT-6 Luna makes focused, repeatable tasks practical at high volume, and both ship at significantly lower pricing than their GPT-5.6 predecessors. Claude Opus 5.5, meanwhile, does more with fewer tokens than Opus 5 and is tuned for agentic coding and long-running tasks. What I like about all three is that they push toward the same idea: match the model to the job instead of reaching for the biggest one every time. The other thread was observability catching up to this agentic world, including a launch I had the pleasure of writing about myself.

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:

• Introducing Amazon CloudWatch Omni – You can now observe your applications and AI agents together in a single, collaborative experience. Amazon CloudWatch Omni is built on OpenTelemetry, so your existing telemetry shows up with nothing to reconfigure, and your whole team reaches it through one URL with enterprise SSO — no console access required. It auto-discovers your services, maps dependencies, and brings AWS DevOps Agent into investigation sessions to correlate signals and trace root causes. There’s a companion post on the agent-observability side, a deeper dive on the AWS Cloud Operations blog on what observability for the AI era looks like, and the announcement on What’s New with the specifics. If you want the bigger picture, Matt Wood’s Wrong, not broken is a great read on why correctness now has to be measured at the level of the run.

• Enhanced custom event buses in Amazon EventBridge – Amazon EventBridge now offers an enhanced custom event bus purpose-built for organizations scaling event-driven applications across teams and accounts. You can now deploy a single centralized bus shared across every account in your organization through AWS RAM, with optional event ordering, a simplified Subscriber resource that bundles filtering, targets, and retries, content-based deduplication, and synchronous invocation for targets like AWS Lambda. A new ingress/egress pricing model replaces the compounding cross-account routing charges of multi-bus setups, and your existing buses keep working unchanged as “classic.”

• Amazon SageMaker HyperPod Inference Gateway – You can now front your LLM inference on Amazon SageMaker HyperPod with a Kubernetes-native, GPU-aware routing layer that deploys as a single Amazon EKS managed add-on with zero application changes. Instead of round-robin load balancing, it routes on real-time inference signals — KV cache utilization, queue depth, prefix cache hits, predicted latency, and more — cutting first-token latency by up to 82% in mixed-hardware and bursty scenarios. It works with any OpenAI-compatible model server, including vLLM and SGLang.

• AI agent skills for AWS End User Messaging and Amazon SES – You can now build and send messages by asking your AI coding agent in plain language. Amazon SES and AWS End User Messaging publish AI agent skills for the AWS MCP Server, giving your agent step-by-step, validated guidance for tasks like verifying a sending identity, sending a production email, or building a branded RCS agent with cards and buttons. The skills work with Claude Code, Codex, Cursor, and Kiro, so you can complete messaging workflows without hopping between docs and console screens.

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

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

• Introducing Strands harness – The Strands Agents team released Strands harness, a fully assembled, general-purpose agent harness you can run locally or deploy anywhere, under Apache 2.0. It takes one line of Python or TypeScript to wire up your model of choice across Amazon Bedrock, Anthropic, OpenAI, Google, or a local Ollama model, and it ships with sensible defaults for prompt caching and context management (truncating bulky tool results, compacting when the context window fills up, and keeping memory across runs). The team reports it costs about 28% less than comparable harnesses on the same models while holding accuracy steady.

• Announcing the new AWS Reimagine report on AI – The AWS Executive in Residence team spent nine months interviewing 154 leaders across 27 countries about what separates organizations that turn AI into value from those that don’t. The report is candid (including where AI hasn’t worked at Amazon), and the recurring insight is that once building gets fast, the bottleneck moves to deciding, funding, and governing the work. Well worth a read if you’re thinking about how your teams adopt AI in practice.

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

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

• AWS re:Invent – AWS re:Invent returns to Las Vegas from November 30 to December 4, and session times, locations, and speakers are live. Reserved seating opens October 6, so register now and be ready to claim your spot in chalk talks, workshops, and builders’ sessions.

• AWS Summits – With re:Invent on the horizon, the Summits are wrapping up for the year. The last stop is Dubai (September 30) at the Dubai World Trade Center, with 60+ sessions, an AWS Village, and hands-on workshops.

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here 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!

— Daniel Abib

The post AWS Weekly Roundup: GPT-6 Sol and Luna, Claude Opus 5.5 on Amazon Bedrock, Strands harness, and more (September 28, 2026) appeared first on The NOKIA Blog.

Living in the Netherlands, I spend a fair amount of time on trains, and that is usually where I catch up on what the builder community is writing. Until now, that meant opening a laptop or squinting at a browser tab on my phone. This week I found myself scrolling through trending articles and checking a workshop from the AWS Builder Center mobile app while waiting for a delayed train, and it made those spare twenty minutes very useful. That is why I am glad to open this week with the Builder Center mobile app.

AWS Builder Center is now available as a mobile app on iOS and Android, extending the experience beyond desktop and web. Using your AWS Builder ID, you stay signed in across sessions and can browse trending articles, access 600+ AWS Skill Builder courses, and manage hands-on workshops with free sandbox environments from your mobile device. You can follow AWS Heroes, Community Builders, and User Group Leaders, check Builder Loft event calendars on the go, and receive push notifications for subscribed topics and communities. The app also supports the Wishlist feature for submitting product feedback directly to AWS teams. It is available worldwide on the Apple App Store and Google Play Store.

Builder Center also added two features this week. Polls give you a way to ask the community a question from the Home feed: write a question, add 2 to 5 answer options, set a deadline, and people vote, with results updating live and discussion happening in the comments. Votes are anonymous, and creators see aggregate counts and percentages only. Separately, the Zero to Shipped hackathon is open from September 18 to October 2. You connect your coding agent to AWS, build a real application, and ship it live on AWS for a chance to win a share of a $28,000 prize pool. Five winning projects each receive $5,000 in AWS credits and an AWS Builder swag bundle.

Last week’s launches
Here is what else happened this week.

• Amazon Connect Talent is now generally available – Amazon Connect Talent is an AI-powered hiring solution for talent acquisition teams managing hiring at scale. Informed by decades of Amazon hiring science, it uses AI agents to conduct structured voice interviews, administer evidence-based assessments, and score candidates consistently, so recruiters can focus on final decisions. Candidates interview 24/7 from any device, and recruiters review scores, transcripts, and detailed evaluations the next morning. All candidate data is anonymized during AI evaluation, each competency is scored against a rubric with every score tied to specific evidence from the interview, and recruiters keep final decision authority over every hire. General availability includes competency-based assessments, AI-led voice interviews with adaptive questioning, a brand-customizable mobile-first candidate portal, and admin onboarding tools.
• Amazon Corretto 27 is now generally available – Amazon Corretto 27, a Feature Release version of the no-cost, multi-platform distribution of OpenJDK, is now available for download on Linux, Windows, and macOS, with support through April 2027. Notable features include G1 as the default garbage collector across all environments (JEP 523), post-quantum hybrid key exchange for TLS 1.3 (JEP 527), compact object headers by default for a smaller memory footprint (JEP 534), and JFR in-process data redaction to remove sensitive data from Java Flight Recorder recordings before they leave the JVM (JEP 536). It also continues previews of enhanced pattern matching, structured concurrency, and lazy constants, along with the Vector API incubator.
• Kimi K3 by Moonshot AI is now generally available on Amazon Bedrock – Kimi K3 is now available on Amazon Bedrock for coding and knowledge work. According to Moonshot AI, Kimi K3 is its most capable model and the first open model to reach 2.8 trillion parameters. It combines native vision capabilities with a 1-million-token context window, making it well suited to long-running coding sessions across large repositories, multi-document analysis, and extended agent workflows. Moonshot AI reports an approximate 2.5x improvement in scaling efficiency over Kimi K2. Kimi K3 is the first open-weight model on Amazon Bedrock to support explicit prompt caching, which helps reduce latency and input costs when reusing context across model calls.
• AWS reimagines the getting started experience – We announced a new simplified experience for builders starting a new project. Instead of completing configuration tasks first, you start with sensible defaults: sign up using an existing identity from providers including Google, GitHub, and Apple, and for most new customers no credit card is required, with $100 in free credits as part of the AWS Free Tier. AWS organizes your work in a project, which contains an AWS account and sharing settings, and applies security controls for you. You can invite collaborators by email without setting up IAM users, set a monthly spend limit starting at $20, and activate advanced AWS features later at no additional cost with no migration. The experience is gradually rolling out to new customers.
• New low-cost burstable Amazon EC2 T8i instances are generally available – Amazon EC2 T8i instances, powered by custom sixth-generation Intel Xeon Scalable processors (Granite Rapids), are among the lowest-cost EC2 instances and deliver up to 30% better price performance over previous-generation T3 instances. They are designed for low-to-moderate CPU utilization workloads such as microservices, low-traffic websites, development and testing environments, and small databases. T8i instances deliver up to 70% higher compute performance, up to 1.25x higher network bandwidth, and up to 2.4x higher Amazon EBS bandwidth compared to T3, and they use the same CPU credit system, so upgrading from T3 is straightforward.
• AWS Elastic Beanstalk introduces Cluster Mode – AWS Elastic Beanstalk Cluster Mode is a new fully managed option for teams running a portfolio of applications on shared infrastructure powered by Amazon EKS. Instead of operating each application in isolation, you run multiple applications through one experience with a single operational baseline, so per-application cost decreases as your portfolio grows. You can upload source code in Java, .NET, Python, Node.js, PHP, Ruby, or Go, and Elastic Beanstalk handles containerization automatically through Cloud Native Buildpacks when needed. Cluster Mode includes production-grade deployment strategies with automatic rollback, event-driven autoscaling, AWS Secrets Manager integration, native OpenTelemetry observability, and AI-powered troubleshooting. Standard and Cluster Mode environments run side by side within the same application, so teams can migrate one environment at a time.

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

Other AWS news
Here are some additional posts you may find useful:

• Building in the AWS European Sovereign Cloud – Two new posts cover building on the AWS European Sovereign Cloud, an independent cloud for Europe that runs as a distinct partition with its own control plane, IAM, billing, console, and service endpoints, and its first Region in Brandenburg, Germany. The first post walks through architecting a secure landing zone, covering account structure and governance, identity as infrastructure as code, centralized logging, data protection, and partition-aware ARN construction that works across AWS partitions. The second announces the general availability of Gemma 4 open-weight models on the Amazon Bedrock next-generation inference engine in the AWS European Sovereign Cloud, with inference staying entirely within eusc-de-east-1 under a zero data retention and zero operator access model.
• The new AgentCore runtime: elastic, optimized, and consistently fast starts – We announced a new version of the Amazon Bedrock AgentCore runtime, the managed compute layer for running agents. The new runtime reclaims memory as a session releases it rather than holding it at the peak, so the bill tracks real usage over the life of a session. It also delivers consistent cold start times regardless of container image size or concurrency by preparing the environment once, snapshotting it, and restoring that snapshot for each new instance. In testing with an empty echo agent, the new runtime delivered a P75 cold start of about 2 seconds from a 200 MB image up to 2 GB, compared to roughly 5.4 to nearly 30 seconds for the original runtime.
• Introducing the updated AWS Well-Architected Streaming Media Lens – We published a revised Streaming Media Lens, which provides architectural best practices for video streaming workloads. The revision expands from the original 2021 version to cover five streaming scenarios, including interactive live streaming with Amazon IVS Real-Time Streaming for up to 25,000 concurrent viewers, low-latency live streaming, and ad-supported content monetization, alongside enhanced video-on-demand and live streaming guidance. It also adds new sustainability best practices focused on reducing carbon footprint, expanded observability and incident-response frameworks, and advanced content protection with multi-layered DRM and forensic watermarking. The lens whitepaper and custom lens are available now.

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

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

• AWS re:Invent – AWS re:Invent returns to Las Vegas from November 30 to December 4. 2, 200+ session times, locations, and speakers are live. Reserved seating for AWS re:Invent opens October 6. Register now and be ready to claim your spot in chalk talks, workshops, and builders’ sessions when reserved seating opens.
• AWS Summits – AWS Summits are free in-person events covering cloud and AI. With re:Invent on the horizon, the Summits are coming to an end for the year. The last Summit is Dubai (September 30) at the Dubai World Trade Center, with 60+ sessions, an AWS Village, and hands-on workshops.
• AWS Community Days – Community-led conferences planned and delivered by community leaders. Upcoming events include Lebanon (September 26), Malaysia, Kuala Lumpur (September 26), Cebu, Philippines (September 26), Davao, Philippines (September 26), ComSum Manchester, UK (October 1), and Italy, Rome (October 2).

Summer has officially given way to September, but the weather where I am has not quite caught up. The days are still unusually warm, and I suspect these are the last mild afternoons before autumn settles in for good. I am making the most of them while they last. Come back next week for more!

— Esra

The post AWS Weekly Roundup: AWS Builder Center mobile apps, Amazon Connect Talent GA, Amazon Corretto 27, and more (September 21, 2026) appeared first on The NOKIA Blog.

Last week, Claude Fable 5.1 became available on AWS. According to Anthropic, Claude Fable 5.1 delivers frontier intelligence for ambitious tasks across coding, scientific research, and enterprise workflows. Claude Fable 5.1 is built for long-running, high-stakes work that runs for hours and spans many applications. It can own more of a software project on its own, handling features across an entire codebase, code review, and performance work over extended sessions.

Anthropic has designated Fable 5.1 a Covered Model, a category of Claude models that carry additional data retention, safety review, and access policies wherever they’re offered. Claude Fable 5.1 is subject to data retention for up to 30 days and human review by Amazon personnel, with a new aws_review data retention mode. In this mode, AWS retains your prompts and outputs for human safety review within the AWS boundary. The provider_data_share mode is legacy, and Amazon Bedrock does not share your data with the model provider. In addition, Enterprise Frontier Safeguards (EFS), built in partnership between AWS and Anthropic, will let eligible customers use Covered Models while keeping their data in a cloud environment they control.

You have two ways to access Claude Fable 5.1: Amazon Bedrock and Claude Platform on AWS. To learn more, see the Claude Fable 5.1 model card on Amazon Bedrock and Claude Platform on AWS.

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

• Amazon Linux 2027 (AL2027) in public preview: AL2027 is the next version of the Amazon Linux operating system. It runs on kernel 7.1+, purpose-built for cloud-native workloads on AWS with performance, scale, and security in mind. Built on AL2023’s baseline, AL2027 is designed for customers who need a secure, stable, and AWS-native operating system running web applications, databases, containerized microservices, AI/ML workloads, and large-scale infrastructure.
• Amazon EC2 R9g and R9gd memory-optimized instances: These instances are powered by AWS Graviton5 processors, delivering the best price performance for memory-intensive workloads running on Amazon EC2. R9g and R9gd instances deliver up to 25% better compute performance compared to AWS Graviton4-based R8g and R8gd instances. They are up to 30% faster for databases, up to 35% faster for web applications, and up to 35% faster for machine learning. To learn more, read Daniel’s blog post.
• AWS Lambda SnapStart for container image functions: Lambda SnapStart is an opt-in capability that makes it easier for you to build highly responsive and scalable applications without provisioning resources or implementing complex performance optimizations. Previously, SnapStart was only supported for managed runtimes (Python, .NET, and Java). You can now use SnapStart for container images to reduce startup times from several seconds to as low as sub-second for latency-sensitive workloads such as ML inference and interactive APIs.
• AWS Agent Registry now generally available: AWS Agent Registry provides a private, governed catalog and discovery layer for agents, tools, skills, MCP servers, and custom resources within your organization. In addition to the capabilities launched in preview (manual and URL-based record creation, approval workflows, semantic and keyword search, and AWS CloudTrail audit trails), Registry now adds new enterprise features. To learn more, visit the AI Blog post.
• Amazon Redshift now supports Apache Iceberg v3 tables: You can read from and write to Apache Iceberg v3 tables in your data lake of Amazon Redshift. With this launch, Amazon Redshift introduces support for default column values, row lineage, and deletion vectors. Amazon Redshift’s Graviton based provisioned and serverless clusters support the new v3 format. To learn more, visit Apache Iceberg v3 features in Redshift.

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:

• AWS named a Leader in the 2026 Gartner Magic Quadrant for Strategic Cloud Platform Services: For the 16th consecutive year, Gartner has recognized AWS as a Leader in the 2026 Gartner Magic Quadrant for Strategic Cloud Platform Services, and once again placed AWS highest on the Ability to Execute axis. We believe this recognition reflects our commitment to delivering the broadest and deepest set of cloud capabilities from infrastructure and AI to security and operations, so you can build, innovate, and scale with confidence.
• AWS Certified AI Business Strategist: This new certification targets professionals who evaluate, champion, and scale AI initiatives in their organizations: line-of-business leaders driving adoption across their teams, sales professionals articulating AI value to customers, consultants guiding client strategy from experimentation through production, program managers aligning AI investments to business outcomes. Beta exam registration opened September 1, 2026, with exam delivery beginning September 29.
• Agentic Security: Detection and Response at Machine Speed: We believe security should evolve ahead of AI adoption, not behind it. That belief drove our team to collaborate with the SANS Institute on a new chapter in the 2026 Cloud Security Exchange eBook, where we lay out a practical framework for securing agentic workloads at enterprise scale. Our chapter goes deeper on securing agentic workloads, with specific architectural patterns, implementation guidance, and frameworks for security teams at every stage of agentic AI maturity, whether you’re evaluating, piloting, or operating at scale.

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 re:Invent, 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: Claude Fable 5.1 on AWS, Amazon Linux 2027 preview, AWS Certified AI Business Strategist, and more (September 7, 2026) appeared first on The NOKIA Blog.

Twenty years ago today, Jeff Barr wrote a blog post that launched the Amazon EC2 Beta. That single post introduced resizable Linux virtual servers in the cloud, billed by the hour, with one instance type (m1.small) in one Region (US East). It was minimal yet useful, and it changed how the world thinks about computing infrastructure.

In 2021, Jeff covered the fifteen years of EC2 with the backstory and memorable EC2 launches. Over the last five years, AWS has continued to push the boundaries of what cloud computing can deliver, building custom silicon for general-purpose and AI workloads and expanding EC2 into new form factors and deployment models that our customers in 2006 could not have imagined.

The 20 years in brief
In his 15th anniversary post, Jeff chose important milestones of EC2 that established the foundational building blocks that customers still rely on today. Amazon Elastic Block Store (2008) provided persistent block storage. Elastic Load Balancing, Auto Scaling, and Amazon CloudWatch (2009) made applications scalable and highly available. Amazon Virtual Private Cloud (2009) gave customers logically isolated networks. AWS Nitro System (2017) enabled faster innovation and enhanced security. AWS Graviton processors (2018) were designed for cost-sensitive scale-out workloads.

Over 20 years, EC2 grew from one to over 1,200 instance types to meet customer needs across general-purpose, compute-, memory-, and storage-optimized, accelerated computing, and high-performance computing families. These instances expanded from one AWS Region to 39 Regions globally. AWS also extended EC2 beyond the Region boundary with AWS Outposts (2018) running EC2 instances locally, AWS Local Zones (2019) place globally, and AWS Wavelength (2019) inside global 5G telecommunications carrier networks.

While I hate to play favorites, I want to choose some of my favorite EC2 launches of the past five years:

• AWS Inferentia for ML inference at scale (2019): We introduced purpose-built ML inference instances (inf1) with AWS Inferentia chips. Amazon EC2 Inf2 instances became generally available in April 2023 for large-scale generative AI inference workloads. Together with the Inferentia family, AWS Trainium instances now give customers a full stack of AWS-designed silicon optimized for every phase of the AI lifecycle both inference and training.
• EC2 Mac instances (2020): The first Mac instances (mac1) were built on Apple Mac mini with Intel Core i7 (Coffee Lake) on the AWS Nitro System. Mac M1 (mac2) instances launched in July 2022 as the first Arm-based macOS instances on EC2. M2 Pro Mac instances followed in 2023, M4 and M4 Pro Mac instances in 2025, M3 Ultra Mac instances and M4 Max Mac instances in 2026, giving Apple developers a complete range of cloud-based build and test environments for macOS, iOS, iPadOS, tvOS, watchOS, and visionOS apps.
• AWS Trainium for full-stack AI workloads at scale (2021): In November 2021, we previewed Trn1 instances with AWS Trainium accelerators optimized for high-performance deep learning training. In December 2024, Trn2 instances powered by AWS Trainium2 launched, with Trn2 UltraServers linking 64 Trainium2 accelerators via NeuronLink for training trillion-parameter foundation models. At AWS re:Invent 2025, Trn3 UltraServers powered by AWS Trainium3 deliver the best token economics for next-generation agentic, reasoning, and video generation applications. A single Trn3 UltraServer interconnects up to 144 Trainium3 chips to train and serve the largest frontier models. Now, AWS Trainium3 delivers the leading price-performance for high-performance AI training and inference at scale.
• EC2 Capacity Blocks for ML (2023): This new EC2 usage model further democratizes ML democratizes ML by making it easy to access GPU instances to train and deploy ML and generative AI models. You reserve the GPU capacity you need (initially P5 instances) for a future date and only for the duration you require. In November 2024, EC2 Capacity Blocks for ML added supported for provisioning in a matter of minutes and extending up to six months. Now, EC2 Capacity Blocks for ML supports P6-B300, P6-B200, P5e, P5en, P4d, P4de, Trn1, Trn2, and Trn3 instances in addition to P5.
• AWS Graviton5 (2025): Building on eight years of Graviton innovation since 2018, we previewed Graviton5 chips in AWS re:Invent 2025 and launched M9g and M9gd instances powered by Graviton5 and built on the sixth-generation AWS Nitro System. C9g and C9gd followed in June 2026. Now, Graviton5 features 192 cores, a 5x larger cache, and up to 33% lower inter-core latency, making it well suited for the growing demands of agentic AI workloads such as real-time reasoning, code generation, and multi-step task orchestration that require continuous, high-throughput CPU compute at scale.
• AWS Nitro Isolation Engine (2026): Customers wanted to see, not just hear from us, proof of workload isolation in the Nitro Hypervisor. The Nitro Isolation Engine is a purpose-built component inside the Nitro Hypervisor, harnessing formal verification to provide mathematical assurance that customer workloads are isolated from each other and AWS operators, pioneering a new standard for mathematically proven cloud security. This feature is also based on the sixth-generation AWS Nitro System which has continued to evolve since our introduction in 2017.

The foundation underneath it all
Despite two decades of innovation, the fundamental value proposition of Amazon EC2 has not changed. Customers use it to get secure, resizable compute capacity in minutes, pay only for what they consume, and scale on demand without long-term commitments. That same flexibility now extends to AI workloads at a scale no one anticipated in 2006.

EC2 remains the foundational compute layer of AWS. Amazon ECS, Amazon EKS, AWS Lambda, AWS Fargate, AWS Batch, Amazon EMR, Amazon SageMaker AI, and Amazon Bedrock ultimately run on EC2 capacity. Every architectural pattern customers have built over the past twenty years, from simple web servers to trillion-parameter foundation model training clusters, starts with a decision to launch an instance.

We made strong foundational decisions in 2006, and we left room for the service to grow. Twenty years later, that strategy of creating services that are minimal-yet-useful, launching quickly, and iterating rapidly in response to your feedback continues to guide how we build. The next twenty years of cloud computing will demand capabilities we have not yet imagined. Amazon EC2 will continue to be the foundation where your workloads run.

To learn more about Amazon EC2, visit the Amazon EC2 product page or check out what’s new with EC2.

— Channy

The post Happy 20th Birthday, Amazon EC2 appeared first on The NOKIA Blog.

It has been a busy stretch on the AWS Summit circuit. At the New York City Summit, I delivered a workshop called Building AI architectures with AWS Serverless, and it was a lot of fun watching builders wire up agents and serverless services to solve real problems in a single afternoon. This week I am heading down to the Washington, DC Summit, which always puts a spotlight on innovation in the public sector. If you are going to be there, come say hello.

A question I hear a lot at these events is how teams can put AI to work without waiting on a long engineering backlog, and this week’s biggest launch speaks directly to that, with Amazon Connect Customer introducing a no-code way for business teams to design AI powered customer experiences themselves. Now, let’s get into this week’s AWS news.

Headlines


Amazon Connect Customer launched the Agentic CX designer (NLX) in preview, a no-code canvas for designing and deploying AI powered self service experiences. Business teams can build and launch voice and digital experiences that bring agentic and deterministic AI together in one governed flow, going from design to testing and simulation to production ready experiences in weeks rather than months. The launch also includes Live Sync in preview, a patented technology that drives a customer’s web or mobile experience in real time as they speak or type. A caller can complete a form or pull up the right product page without ever leaving the conversation. To see how this reshapes who designs customer experience, read the blog post on how the business user is the new architect of customer experience.

Last week’s launches


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

• AWS Lambda MicroVMs – A new serverless compute primitive that gives each user or job VM level isolation with near instant launch and resume speeds, plus the ability to suspend and resume execution for up to 8 hours. Built on Firecracker, it is made for running user or AI generated code in multi-tenant applications without managing virtualization infrastructure or trading off isolation, speed, and state.
• Amazon EC2 AMI Watermarks – Lets you embed custom identifiers in your private AMIs that automatically carry forward to every derived AMI across copies, Regions, and account shares. You can combine watermarks with Allowed AMIs and Declarative Policies to restrict launches to approved images, available at no additional cost in all AWS Regions.
• AWS Outposts self-service lifecycle management – Adds self service configuration, quoting, ordering, subscription management, renewal, and decommissioning directly from the console, CLI, and API. A new quoting tool generates real time cost estimates in seconds and surfaces account and regional constraints before you submit an order.
• Amazon MSK AI Agent Skills – Gives AI coding assistants like Kiro, Claude Code, and Cursor expert, up-to-date guidance for operating Amazon MSK, covering troubleshooting, sizing, configuring, monitoring, and migrating external Kafka clusters to MSK Express. Tasks that once required specialized knowledge become a guided experience developers can complete on their own.
• Amazon OpenSearch Service AI-assisted migrations – Migration Assistant now includes an agent guided experience that helps you move self managed Apache Solr, Elasticsearch, or OpenSearch deployments to OpenSearch Serverless or Managed Clusters using tools like Kiro and Claude Code, with new live traffic capture and replay support for Solr.
• Amazon GuardDuty AI-powered investigations (preview) – Automatically analyzes findings and accounts to help you separate true threats from benign activity, examining context and related activity from the last 90 days with knowledge graphs and threat intelligence. Each investigation returns a disposition assessment with confidence scoring, MITRE ATT&CK classification, and actionable recommendations in minutes.

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

Other AWS news


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

• Open Governance for MySQL – Oracle announced a community governance model for MySQL that gives organizations outside Oracle a defined role in the project, including four non Oracle seats on a new Steering Committee and a public GitHub presence. AWS holds a seat and shares why it supports the move and how it already contributes fixes upstream for everyone running MySQL.
• A new way to keep your AWS Certification current -You can now maintain an eligible AWS Certification for an additional year by completing curated training and hands on labs on AWS Skill Builder instead of retaking a full exam. The option is available today in open beta for several Associate and Professional certifications, with more coming later this year.
• The All Builders Welcome Grant insider’s guide for 2026 applicants – A community guide on AWS Builder Center that walks early career builders through applying for the grant, which covers a full conference pass, airfare, and hotel for AWS re:Invent 2026. Applications are open now and close on July 14.

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

Looking for ways to connect with builders in person? Check out the AWS Summits coming to a city near you, find a local AWS Community Day led by user groups around the world, and explore tutorials, community content, and ways to grow your skills over at the AWS Builder Center.

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

-Micah

The post AWS Weekly Roundup: Agentic CX designer for Amazon Connect Customer, EC2 AMI Watermarks, Open Governance for MySQL, and more (June 29, 2026) appeared first on The NOKIA Blog.

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.

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.

We’re thrilled to celebrate three exceptional developer community leaders as AWS Heroes. These individuals represent the heart of what makes the AWS community so vibrant. In addition to sharing technical knowledge, they build connections, forge genuine human relationships, and create pathways for others to grow. From pioneering cloud culture in mountain villages to leading cybersecurity education across continents, these Heroes demonstrate that true leadership extends beyond technical expertise to the communities we build and the lives we impact.

Maurizio Argoneto – Pignola, Italy

Community Hero Maurizio is a CTO and organizer of the AWS User Group Basilicata, recognized for his dedication to building tech ecosystems where they previously did not exist. For over a decade, he has pioneered cloud culture through a philosophy centered on genuine human connection and knowledge transfer. He founded an international tech conference in a small mountain village, creating a unique space where global experts and local talent meet, blending deep technical sessions on cloud architectures, DevOps, and web scaling with unconventional networking experiences. Beyond organizing events, Maurizio is a tireless mentor working across generations, which span from introducing children to coding to helping university students and professionals transition into cloud architecture. His impact is defined by a rare combination of technical leadership and inclusive community building that draws people from across Europe.

Ray Goh – Singapore

Artificial Intelligence Hero Ray Goh is a seasoned AWS machine learning and AI community leader based in Singapore and a long-standing contributor in various AWS community programs since 2018, from AWS ASEAN Cloud Warrior and AWS Dev/Cloud Alliance to being part of the pioneer batch of AWS Community Builders in 2020. He founded The Gen-C (a Generative AI Learning Community) in 2024, organizing regular public workshops at libraries across Singapore on topics ranging from LLM fine-tuning to AI agents on AWS. Ray has spoken at AWS re:Invent, AWS Summit ASEAN, AWS Community Day Hong Kong, and numerous user group meetups, and guest-authored for the AWS Machine Learning Blog. He spearheaded the world’s largest enterprise AWS DeepRacer program for DBS Bank in 2020, upskilling over 3,100 employees, and trained more than 1,300 ASEAN students in LLM techniques in 2025. His community work extends to skills-based CSR initiatives teaching AI and machine learning to women, children, and youths, with contributions featured on CNBC and Euromoney.

Sheyla Leacock – Panama City, Panama

Security Hero Sheyla Leacock is an IT security professional, mentor, technical author, and international speaker contributing to the global cloud and cybersecurity community. She has spoken at AWS Summit Mexico, AWS Summit LATAM in Peru, and led PeerTalk sessions at AWS re:Invent, while also leading the AWS User Group in Panama and regularly participating in AWS Community Days and regional meetups. Beyond AWS-focused events, she has delivered talks at more than 20 international conferences and publishes technical articles and educational content on AWS cloud computing and cybersecurity. She collaborates with universities as a guest lecturer, supporting the development of emerging technology and cybersecurity talent. Through community leadership, knowledge sharing, and education, she contributes to strengthening the AWS and cybersecurity ecosystem.

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 Our First 2026 AWS Heroes Cohort Is Here! 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.

Hey! It’s my first post for 2026, and I’m writing to you while watching our driveway getting dug out. I hope wherever you are you are safe and warm and your data is still flowing!

This week brings exciting news for customers running GPU-intensive workloads, with the launch of our newest graphics and AI inference instances powered by NVIDIA’s latest Blackwell architecture. Along with several service enhancements and regional expansions, this week’s updates continue to expand the capabilities available to AWS customers.

Last week’s launches

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

• Amazon EC2 G7e instances are now generally available — The new G7e instances accelerated by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs deliver up to 2.3 times better inference performance compared to G6e instances. With two times the GPU memory and support for up to 8 GPUs providing 768 GB of total GPU memory, these instances enable running medium-sized models of up to 70B parameters with FP8 precision on a single GPU. G7e instances are ideal for generative AI inference, spatial computing, and scientific computing workloads. Available now in US East (N. Virginia) and US East (Ohio).
• Amazon Corretto January 2026 Quarterly Updates — AWS released quarterly security and critical updates for Amazon Corretto Long-Term Supported (LTS) versions of OpenJDK. Corretto 25.0.2, 21.0.10, 17.0.18, 11.0.30, and 8u482 are now available, ensuring Java developers have access to the latest security patches and performance improvements.
• Amazon ECR now supports cross-repository layer sharing — Amazon Elastic Container Registry now enables you to share common image layers across repositories through blob mounting. This feature helps you achieve faster image pushes by reusing existing layers and reduce storage costs by storing common layers once and referencing them across repositories.
• Amazon CloudWatch Database Insights expands to four additional regions — CloudWatch Database Insights on-demand analysis is now available in Asia Pacific (New Zealand), Asia Pacific (Taipei), Asia Pacific (Thailand), and Mexico (Central). This feature uses machine learning to help identify performance bottlenecks and provides specific remediation advice.
• Amazon Connect adds conditional logic and real-time updates to Step-by-Step Guides — Amazon Connect Step-by-Step Guides now enables managers to build dynamic guided experiences that adapt based on user interactions. Managers can configure conditional user interfaces with dropdown menus that show or hide fields, change default values, or adjust required fields based on prior inputs. The feature also supports automatic data refresh from Connect resources, ensuring agents always work with current information.

Upcoming AWS events

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

• Best of AWS re:Invent (January 28-29, Virtual) — Join us for this free virtual event bringing you the most impactful announcements and top sessions from AWS re:Invent. AWS VP and Chief Evangelist Jeff Barr will share highlights during the opening session. Sessions run January 28 at 9:00 AM PT for AMER, and January 29 at 9:00 AM SGT for APJ and 9:00 AM CET for EMEA. Register to access curated technical learning, strategic insights from AWS leaders, and live Q&A with AWS experts.
• AWS Community Day Ahmedabad (February 28, 2026, Ahmedabad, India) — The 11th edition of this community-driven AWS conference brings together cloud professionals, developers, architects, and students for expert-led technical sessions, real-world use cases, tech expo booths with live demos, and networking opportunities. This free event includes breakfast, lunch, and exclusive swag.

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

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

~ micah

The post AWS Weekly Roundup: Amazon EC2 G7e instances, Amazon Corretto updates, and more (January 26, 2026) appeared first on The NOKIA Blog.

At the end of 2025 I was happy to take a long break to enjoy the incredible summers that the southern hemisphere provides. I’m back and writing my first post in 2026 which also happens to be my last post for the AWS News Blog (more on this later).

The AWS community is starting the year strong with various AWS re:invent re:Caps being hosted around the globe, with some communities already hosting their AWS Community Day events, the AWS Community Day Tel Aviv 2026 was hosted last week.

Last week’s launches

Here are last week’s launches that caught my attention:

• Kiro CLI latest features – Kiro CLI now has granular controls for web fetch URLs, keyboard shortcuts for your custom agents, enhanced diff views, and much more. With these enhancements, you can now use allowlists or blocklists to restrict which URLs the agent can access, ensure a frictionless experience when working with multiple specialized agents in a single session, to name a few.
• AWS European Sovereign Cloud – Following an announcement in 2023 of plans to build a new, independent cloud infrastructure, last week we announced the general availability of the AWS European Sovereign Cloud to all customers. The cloud is ready to meet the most stringent sovereignty requirements of European customers with a comprehensive set of AWS services.
• Amazon EC2 X8i instances – Previously launched in preview at AWS re:Invent 2025, last week we announced the general availability of new memory-optimized Amazon Elastic Compute Cloud (Amazon EC2) X8i instances. These instances are powered by custom Intel Xeon 6 processors with a sustained all-core turbo frequency of 3.9 GHz, available only on AWS. These SAP certified instances deliver the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud.

Additional updates

These projects, blog posts, and news articles also caught my attention:

• 5 core features in Amazon Quick Suite – AWS VP Agentic AI Swami Sivasubramanian talks about how he uses Amazon Quick Suite for just about everything. In October 2025 we announced Amazon Quick Suite, a new agentic teammate that quickly answers your questions at work and turns insights into actions for you. Amazon Quick Suite has become one of my favorite productivity tools, helping me with my research on various topics in addition to providing me with multiple perspectives on a topic.
• Deploy AI agents on Amazon Bedrock AgentCore using GitHub Actions – Last year we announced Amazon Bedrock AgentCore, a flexible service that helps you seamlessly create and manage AI agents across different frameworks and models, whether hosted on Amazon Bedrock or other environments. Learn how to use a GitHub Actions workflow to automate the deployment of AI agents on AgentCore Runtime. This approach delivers a scalable solution with enterprise-level security controls, providing complete continuous integration and delivery (CI/CD) automation.

Upcoming AWS events

Join us January 28 or 29 (depending on your time zone) for Best of AWS re:Invent, a free virtual event where we bring you the most impactful announcements and top sessions from AWS re:Invent. Jeff Barr, AWS VP and Chief Evangelist, will share his highlights during the opening session.

There is still time until January 21 to compete for $250,000 in prizes and AWS credits in the Global 10,000 AIdeas Competition (yes, the second letter is an I as in Idea, not an L as in like). No code required yet: simply submit your idea, and if you’re selected as a semifinalist, you’ll build your app using Kiro within AWS Free Tier limits. Beyond the cash prizes and potential featured placement at AWS re:Invent 2026, you’ll gain hands-on experience with next-generation AI tools and connect with innovators globally.

Earlier this month, the 2026 application for the Community Builders program launched. The application is open until January 21st, midnight PST so here’s your last chance to ensure that you don’t miss out.

If you’re interested in these opportunities, join the AWS Builder Center to learn with builders in the AWS community.

With that, I close one of my most meaningful chapters here at AWS. It’s been an absolute pleasure to write for you and I thank you for taking the time to read the work that my team and I pour our absolute hearts into. I’ve grown from the close collaborations with the launch teams and the feedback from all of you. The Sub-Sahara Africa (SSA) community has grown significantly, and I want to dedicate more time focused on this community, I’m still at AWS and I look forward to meeting at an event near you!

Check back next Monday for another Weekly Roundup!

– Veliswa Boya

The post AWS Weekly Roundup: Kiro CLI latest features, AWS European Sovereign Cloud, EC2 X8i instances, and more (January 19, 2026) appeared first on The NOKIA Blog.

Since a preview launch at AWS re:Invent 2025, we’re announcing the general availability of new memory-optimized Amazon Elastic Compute Cloud (Amazon EC2) X8i instances. These instances are powered by custom Intel Xeon 6 processors with a sustained all-core turbo frequency of 3.9 GHz, available only on AWS. These SAP certified instances deliver the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud.

X8i instances are ideal for memory-intensive workloads including in-memory databases such as SAP HANA, traditional large-scale databases, data analytics, and electronic design automation (EDA), which require high compute performance and a large memory footprint.

These instances provide 1.5 times more memory capacity (up to 6 TB), and 3.4 times more memory bandwidth compared to previous generation X2i instances. These instances offer up to 43% higher performance compared to X2i instances, with higher gains on some of the real-world workloads. They deliver up to 50% higher SAP Application Performance Standard (SAPS) performance, up to 47% faster PostgreSQL performance, up to 88% faster Memcached performance, and up to 46% faster AI inference performance.

During the preview, customers like RISE with SAP utilized up to 6 TB of memory capacity with 50% higher compute performance compared to X2i instances. This enabled faster transaction processing and improved query response times for SAP HANA workloads. Orion reduced the number of active cores on X8i instances compared to X2idn instances while maintaining performance thresholds, cutting SQL Server licensing costs by 50%.

X8i instances

X8i instances are available in 14 sizes including three larger instance sizes (48xlarge, 64xlarge, and 96xlarge), so you can choose the right size for your application to scale up, and two bare metal sizes (metal-48xl and metal-96xl) to deploy workloads that benefit from direct access to physical resources. X8i instances feature up to 100 Gbps of network bandwidth with support for the Elastic Fabric Adapter (EFA) and up to 80 Gbps of throughput to Amazon Elastic Block Store (Amazon EBS).

Here are the specs for X8i instances:

X8i instances support the instance bandwidth configuration (IBC) feature like other eighth-generation instance types, offering flexibility to allocate resources between network and EBS bandwidth. You can scale network or EBS bandwidth by up to 25%, improving database performance, query processing speeds, and logging efficiency. These instances also use sixth-generation AWS Nitro cards, which offload CPU virtualization, storage, and networking functions to dedicated hardware and software, enhancing performance and security for your workloads.

Now available

Amazon EC2 X8i instances are now available in US East (N. Virginia), US East (Ohio), US West (Oregon), and Europe (Frankfurt) AWS Regions. For Regional availability and a future roadmap, search the instance type in the CloudFormation resources tab of AWS Capabilities by Region.

You can purchase these instances as On-Demand Instances, Savings Plan, and Spot Instances. To learn more, visit the Amazon EC2 Pricing page.

Give X8i instances a try in the Amazon EC2 console. To learn more, visit the Amazon EC2 X8i instances page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Channy

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