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.

The news that interested me the most last week was the DuckLabs acquisition. AWS has signed a definitive agreement to acquire DuckLabs, the Amsterdam-based company behind DuckDB, the popular open source analytical database that runs in-process and executes SQL directly against files like Parquet, CSV, and JSON. DuckDB stays open source under its independent foundation and the MIT license, and over time AWS plans to combine its speed at everyday queries with the enterprise scale of services like Amazon S3, Amazon Redshift, and Amazon Athena.

Co-founded by Hannes Mühleisen and Mark Raasveldt, DuckDB runs locally or on Amazon S3, which makes it remarkably fast for the everyday queries (a terabyte or less) that make up the bulk of real-world analytics. It also happens to pair beautifully with AI agents, which “poke” and experiment their way through data much like humans do. The co-founders will continue leading its technical direction while AWS combines DuckDB’s speed with analytics services like Amazon EMR, AWS Glue, and Amazon SageMaker. For the bigger picture on why this matters, Andy Warfield, Vice President and Distinguished Engineer shared his thoughts on the post DuckDB and the changing physics of analytics on All Things Distributed.

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

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

• Amazon ECS now automatically detects and recovers container instances that lose agent connectivity – Amazon ECS now continuously monitors agent connectivity to the control plane and surfaces a new AGENT_CONNECTIVITY health event across AWS Fargate, Amazon ECS Managed Instances, and Amazon ECS on EC2. On Fargate and Managed Instances, ECS handles recovery automatically, draining tasks, launching replacements, and deregistering the impaired instance. On EC2, you can wire the event into your own workflow. Available at no additional cost in all AWS Commercial and AWS GovCloud (US) Regions.
• AWS Lambda introduces public preview runtimes, starting with Node.js 26 and Python 3.15 – You can now test upcoming Lambda runtimes before they reach general availability. Preview runtimes use the same identifier as the eventual GA version, so your functions graduate automatically with no action required. Third-party tools and deployment frameworks can also validate compatibility ahead of GA. Not meant for production yet (breaking changes are possible), but a great way to get ahead of your next upgrade. Available in all AWS commercial, AWS GovCloud (US), and China Regions.
• AWS IoT Core adds a native InfluxDB rule action – You can now route time-series data from your IoT devices straight into InfluxDB (Amazon Timestream-managed or self-hosted) without writing custom code or standing up an intermediate service. IoT Core formats data into InfluxDB’s line protocol and supports device-side and server-side batching. Available in all AWS Regions where Amazon Timestream for InfluxDB is offered.
• Amazon GameLift Servers now includes enhanced DDoS protection – Your game servers now get automatic protection against network and transport layer (layers 3 and 4) DDoS attacks – UDP reflection, SYN floods, and similar vectors – with nothing to enable or opt into. Built on top of AWS Shield Standard with gaming-optimized traffic shaping, it turns on the moment your servers start running (Server SDK 5) at no extra cost. It’s available in all supported GameLift Servers Regions except China (Beijing) and China (Ningxia).
• Amazon SageMaker HyperPod expands support for Ray – You can now run Ray workloads on SageMaker HyperPod with built-in observability, resilient training, and accelerated inference. Create and manage Ray clusters from Amazon SageMaker Studio, attach JupyterLab or your local IDE so a multi-node cluster behaves like a local dev environment, and get auto-provisioned Grafana dashboards. Node auto recovery, hung job detection, and tiered checkpointing keep large training runs healthy, while Ray Serve adds a tiered KV cache for inference. Your existing open source Ray code runs unchanged. Available for HyperPod clusters orchestrated by Amazon EKS.

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:

• Happy 20th birthday, Amazon EC2! – Amazon EC2 turns 20. Channy Yun looks back at how EC2 grew from a single m1.small instance type in one Region to more than 1,200 instance types across 39 Regions, along with the custom silicon journey from the first Graviton to Graviton5 and Trainium3. A fun and worthwhile read on the service that still underpins so much of AWS – including Amazon ECS, Amazon EKS, AWS Lambda, Amazon SageMaker, and Amazon Bedrock.
• Agentic Resource Discovery (ARD): an open specification for agent discovery – As organizations scale up agents, tools, and MCP servers, those resources end up scattered across clouds, on-premises infrastructure, and SaaS platforms – each with its own registry and metadata. ARD is a new open specification (Apache 2.0) that defines a common way to describe and discover agentic resources, so publishers “describe once” and consumers “discover everywhere” – think DNS, but for agents. AWS contributed feedback but doesn’t own the spec, and it complements the AWS Agent Registry by letting you federate across catalogs without migrating.
• Get started with the Agent Toolkit for AWS in the AWS CLI – A single AWS CLI command (aws configure agent-toolkit) now equips AI coding agents like Kiro, Claude Code, Codex, and Cursor with curated, up-to-date AWS knowledge and a secure connection to thousands of AWS APIs through the AWS MCP Server. If you build with an AI coding assistant, this helps it choose the right services, use modern APIs, and follow security best practices – so it gets AWS code right more often the first time.

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

• AWS Summits – Free in-person events where builders come together to learn, connect, and explore the latest in cloud and AI. Upcoming stops include Zurich (September 2), São Paulo (September 3), Tel Aviv (September 10), and Dubai (September 30). Can’t attend in person? You can stream sessions through the Global Livestream and On-Demand Hub. I’ll be presenting two sessions on generative AI and Amazon Bedrock at the São Paulo Summit – if you’re there, come say hello.
• AWS Community Days – Community-led conferences where content is planned, sourced, and delivered by community leaders. Upcoming events include JAWS SONIC 2026 in Tokyo (September 5) and Warsaw, Poland (September 8).

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

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: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026) appeared first on The NOKIA Blog.

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

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

Getting started with serverless model customization

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

Customize with UI

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

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

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

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

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

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

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

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

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

Customize with code

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

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

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

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

Now available

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

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

— Channy

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

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

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

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

Checkpointless training: How it works

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

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

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

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

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

Elastic training: How it works

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

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

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

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

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

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

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

Now available

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

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

— Channy

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

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

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

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

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

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

Additional Updates

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

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

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

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

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

Happy building!

— Donnie

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

Let me start this week’s update with something I’m especially excited about – the upcoming BeSA (Become a Solutions Architect) cohort. BeSA is a free mentoring program that I host along with a few other AWS employees on a volunteer basis to help people excel in their cloud careers. Last week, the instructors’ lineup was finalized for the 6-week cohort starting September 6. The cohort will focus on migration and modernization on AWS. Visit the BeSA website to learn more.

Another highlight for me last week was the announcement of six new AWS Heroes for their technical leadership and exceptional contributions to the AWS community. Read the full announcement to learn more about these community leaders.

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

• Amazon EC2 Single GPU P5 instances are now generally available — You can right-size your machine learning (ML) and high performance computing (HPC) resources cost-effectively with the new Amazon Elastic Compute Cloud (Amazon EC2) P5 instance size with one NVIDIA H100 GPU.
• AWS Advanced Go Driver is generally available — You can now use the AWS Advanced Go Driver with Amazon Relational Database Service (Amazon RDS) and Amazon Aurora PostgreSQL-Compatible and MySQL-Compatible database clusters for faster switchover and failover times, Federated Authentication, and authentication with AWS Secrets Manager or AWS Identity and Access Management (IAM). You can install the PostgreSQL and MySQL packages for Windows, Mac, or Linux, by following the installation guides in GitHub.
• Expanded support for Cilium with Amazon EKS Hybrid Nodes — Cilium is a Cloud Native Computing Foundation (CNCF) graduated project that provides core networking capabilities for Kubernetes workloads. Now, you can receive support from AWS for a broader set of Cilium features when using Cilium with Amazon EKS Hybrid Nodes including application ingress, in-cluster load balancing, Kubernetes network policies, and kube-proxy replacement mode.
• Amazon SageMaker AI now supports P6e-GB200 UltraServers — You can accelerate training and deployment of foundational models (FMs) at trillion-parameter scale by using up to 72 NVIDIA Blackwell GPUs under one NVLink domain with the new P6e-GB200 UltraServer support in Amazon SageMaker HyperPod and Model Training.
• Amazon SageMaker HyperPod now supports fine-grained quota allocation of compute resources, topology-aware-scheduling of LLM tasks and custom Amazon Machine Images (AMIs) — You can allocate fine-grained compute quota for GPU, Trainium accelerator, vCPU, and vCPU memory within an instance to optimize compute resource distribution. With topology-aware scheduling, you can schedule your large language model (LLM) tasks on an optimal network topology to minimize network communication and enhance training efficiency. Using custom AMIs, you can deploy clusters with pre-configured, security-hardened environments that meet your specific organizational requirements.

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

• Celebrating 10 years of Amazon Aurora innovation — Join the livestream event on August 21, 2025, to celebrate a decade of Aurora database innovation.
• AWS named as a Leader in 2025 Gartner Magic Quadrant for Strategic Cloud Platform Services — For the fifteenth consecutive year, Gartner has named AWS a Leader in the Gartner Magic Quadrant for Strategic Cloud Platform Services (SCPS), making AWS the longest-running Magic Quadrant Leader.
• Introducing AWS Cloud Control API (CCAPI) MCP Server — You can now use natural language to managing cloud infrastructure using CCAPI MCP Server. You can create, read, update, delete, and list resources using natural language.
• Introducing Amazon Bedrock AgentCore Identity — AgentCore Identity provides a centralized capability to manage agent identities, securing credentials, and supporting seamless integration with AWS and third-party services through Sigv4, standardized OAuth 2.0 flows, and API key.
• Introducing Amazon Bedrock AgentCore Gateway — A fully managed service to connect AI agents with tools and services. It serves as a centralized tool server, providing a unified interface where agents can discover, access, and invoke tools.

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

• AWS re:Invent 2025 (December 1-5, 2025, Las Vegas) — The AWS flagship annual conference offering collaborative innovation through peer-to-peer learning, expert-led discussions, and invaluable networking opportunities.
• AWS Summits — Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Coming up soon are summits in Johannesburg (August 20) and Toronto (September 4).
• AWS Community Days — Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Adria (September 5), Baltic (September 10), Aotearoa (September 18), and South Africa (September 20).

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

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

– Prasad

The post AWS Weekly Roundup: Single GPU P5 instances, Advanced Go Driver, Amazon SageMaker HyperPod and more (August 18, 2025) appeared first on The NOKIA Blog.

Today, we’re announcing a suite of customization capabilities for Amazon Nova in Amazon SageMaker AI. Customers can now customize Nova Micro, Nova Lite, and Nova Pro across the model training lifecycle, including pre-training, supervised fine-tuning, and alignment. These techniques are available as ready-to-use Amazon SageMaker recipes with seamless deployment to Amazon Bedrock, supporting both on-demand and provisioned throughput inference.

Amazon Nova foundation models power diverse generative AI use cases across industries. As customers scale deployments, they need models that reflect proprietary knowledge, workflows, and brand requirements. Prompt optimization and retrieval-augmented generation (RAG) work well for integrating general-purpose foundation models into applications, however business-critical workflows require model customization to meet specific accuracy, cost, and latency requirements.

Choosing the right customization technique
Amazon Nova models support a range of customization techniques including: 1) supervised fine-tuning, 2) alignment, 3) continued pre-training, and 4) knowledge distillation. The optimal choice depends on goals, use case complexity, and the availability of data and compute resources. You can also combine multiple techniques to achieve your desired outcomes with the preferred mix of performance, cost, and flexibility.

Supervised fine-tuning (SFT) customizes model parameters using a training dataset of input-output pairs specific to your target tasks and domains. Choose from the following two implementation approaches based on data volume and cost considerations:

• Parameter-efficient fine-tuning (PEFT) — updates only a subset of model parameters through lightweight adapter layers such as LoRA (Low-Rank Adaptation). It offers faster training and lower compute costs compared to full fine-tuning. PEFT-adapted Nova models are imported to Amazon Bedrock and invoked using on-demand inference.
• Full fine-tuning (FFT) — updates all the parameters of the model and is ideal for scenarios when you have extensive training datasets (tens of thousands of records). Nova models customized through FFT can also be imported to Amazon Bedrock and invoked for inference with provisioned throughput.

Alignment steers the model output towards desired preferences for product-specific needs and behavior, such as company brand and customer experience requirements. These preferences may be encoded in multiple ways, including empirical examples and policies. Nova models support two preference alignment techniques:

• Direct preference optimization (DPO) — offers a straightforward way to tune model outputs using preferred/not preferred response pairs. DPO learns from comparative preferences to optimize outputs for subjective requirements such as tone and style. DPO offers both a parameter-efficient version and a full-model update version. The parameter-efficient version supports on-demand inference.
• Proximal policy optimization (PPO) — uses reinforcement learning to enhance model behavior by optimizing for desired rewards such as helpfulness, safety, or engagement. A reward model guides optimization by scoring outputs, helping the model learn effective behaviors while maintaining previously learned capabilities.

Continued pre-training (CPT) expands foundational model knowledge through self-supervised learning on large quantities of unlabeled proprietary data, including internal documents, transcripts, and business-specific content. CPT followed by SFT and alignment through DPO or PPO provides a comprehensive way to customize Nova models for your applications.

Knowledge distillation transfers knowledge from a larger “teacher” model to a smaller, faster, and more cost-efficient “student” model. Distillation is useful in scenarios where customers do not have adequate reference input-output samples and can leverage a more powerful model to augment the training data. This process creates a customized model of teacher-level accuracy for specific use cases and student-level cost-effectiveness and speed.

Here is a table summarizing the available customization techniques across different modalities and deployment options. Each technique offers specific training and inference capabilities depending on your implementation requirements.

Early access customers, including Cosine AI, Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL), Volkswagen, Amazon Customer Service, and Amazon Catalog Systems Service, are already successfully using Amazon Nova customization capabilities.

Customizing Nova models in action
The following walks you through an example of customizing the Nova Micro model using direct preference optimization on an existing preference dataset. To do this, you can use Amazon SageMaker Studio.

Launch your SageMaker Studio in the Amazon SageMaker AI console and choose JumpStart, a machine learning (ML) hub with foundation models, built-in algorithms, and pre-built ML solutions that you can deploy with a few clicks.

Then, choose Nova Micro, a text-only model that delivers the lowest latency responses at the lowest cost per inference among the Nova model family, and then choose Train.

Next, you can choose a fine-tuning recipe to train the model with labeled data to enhance performance on specific tasks and align with desired behaviors. Choosing the Direct Preference Optimization offers a straightforward way to tune model outputs with your preferences.

When you choose Open sample notebook, you have two environment options to run the recipe: either on the SageMaker training jobs or SageMaker Hyperpod:

Choose Run recipe on SageMaker training jobs when you don’t need to create a cluster and train the model with the sample notebook by selecting your JupyterLab space.

Alternately, if you want to have a persistent cluster environment optimized for iterative training processes, choose Run recipe on SageMaker HyperPod. You can choose a HyperPod EKS cluster with at least one restricted instance group (RIG) to provide a specialized isolated environment, which is required for such Nova model training. Then, choose your JupyterLabSpace and Open sample notebook.

This notebook provides an end-to-end walkthrough for creating a SageMaker HyperPod job using a SageMaker Nova model with a recipe and deploying it for inference. With the help of a SageMaker HyperPod recipe, you can streamline complex configurations and seamlessly integrate datasets for optimized training jobs.

In SageMaker Studio, you can see that your SageMaker HyperPod job has been successfully created and you can monitor it for further progress.

After your job completes, you can use a benchmark recipe to evaluate if the customized model performs better on agentic tasks.

For comprehensive documentation and additional example implementations, visit the SageMaker HyperPod recipes repository on GitHub. We continue to expand the recipes based on customer feedback and emerging ML trends, ensuring you have the tools needed for successful AI model customization.

Availability and getting started
Recipes for Amazon Nova on Amazon SageMaker AI are available in US East (N. Virginia). Learn more about this feature by visiting the Amazon Nova customization webpage and Amazon Nova user guide and get started in the Amazon SageMaker AI console.

–Betty

Updated on July 16, 2025 – Revised the table data and console screenshot.

The post Announcing Amazon Nova customization in Amazon SageMaker AI appeared first on The NOKIA Blog.

Earlier this year, we preannounced that TwelveLabs video understanding models were coming to Amazon Bedrock. Today, we’re announcing the models are now available for searching through videos, classifying scenes, summarizing, and extracting insights with precision and reliability.

TwelveLabs has introduced Marengo, a video embedding model proficient at performing tasks such as search and classification, and Pegasus, a video language model that can generate text based on video data. These models are trained on Amazon SageMaker HyperPod to deliver groundbreaking video analysis that provides text summaries, metadata generation, and creative optimization.

With the TwelveLabs models in Amazon Bedrock, you can find specific moments using natural language video search capabilities like “show me the first touchdown of the game” or “find the scene where the main characters first meet” and instantly jump to those exact moments. You can also build applications to understand video content by generating descriptive text such as titles, topics, hashtags, summaries, chapters, or highlights for discovering insights and connections without requiring predefined labels or categories.

For example, you can find recurring themes in customer feedback or spot product usage patterns that weren’t obvious before. Whether you have hundreds or thousands of hours of video content, you can now transform that entire library into a searchable knowledge resource while maintaining enterprise-grade security and performance.

Let’s take a look at the Marengo and Pegasus videos that TwelveLabs has published.

You can transform video workflows with these models across industries. Media producers and editors can instantly locate specific scenes or dialogue, which means you can focus on storytelling rather than sifting through hours of footage. Marketing teams are streamlining their advertising workflows by quickly personalizing content to resonate with various audiences, while security teams are using the technology to proactively identify potential risks by spotting patterns across multiple video feeds.

Getting started with TwelveLabs models in Amazon Bedrock
Before getting started, if you’re new to using TwelveLabs models, go to the Amazon Bedrock console and choose Model access in the bottom left navigation pane. To access the latest TwelveLabs models, request access for Marengo Embed 2.7 or Pegasus 1.2 in TwelveLabs by available Regions.

To use TwelveLabs models in Amazon Bedrock, choose Chat/Text Playground under Test in the left navigation pane. Choose Select model, select TwelveLabs as the category and Pegasus as the model, and then choose Apply.

For searching or generating text from your video, you should upload your video to an Amazon Simple Storage Service (Amazon S3) bucket or input Base64-formatted video string.

I will use a sample video generated with Amazon Nova Reel with a prompt: “A cute raccoon playing guitar underwater.”

Enter the S3 URI of your uploaded video and the S3 Bucket Owner, and then run your prompt: “Tell me about the video by timeline”.

You can get following model response:

The video opens with an underwater scene featuring a sandy seabed and swaying seaweed, creating a serene atmosphere. At around 5 seconds, a raccoon appears, swimming towards the camera with a curious expression. By 10 seconds, the raccoon is seen swimming near the seabed. The focus then shifts to a group of koi fish swimming gracefully near the seabed from 11 to 13 seconds.

At 14 seconds, the scene transitions to an ukulele resting on the seabed, with the camera zooming in to highlight its details. The raccoon reappears at 20 seconds, swimming among the koi fish, and by 23 seconds, it begins to play the ukulele. The raccoon continues to play the ukulele from 29 to 41 seconds, with the koi fish swimming around it, adding a lively and colorful touch to the underwater setting.

From 42 to 49 seconds, the raccoon is shown playing the ukulele with the koi fish swimming around it. The video concludes with a screen displaying "Video generated with Amazon Novo Reel 1.1" and "Available in Amazon Bedrock" from 50 to 54 seconds, followed by the AWS logo from 55 to 57 seconds.

The TwelveLabs models can be easily integrated into your applications using the Amazon Bedrock Invoke 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 the TwelveLabs Pegasus model:

import boto3
import json
import base64

AWS_REGION = "us-west-2"
MODEL_ID = "us.twelvelabs.pegasus-1-2-v1:0" # OR "eu.twelvelabs.pegasus-1-2-v1:0" if it is doing cross region inference in europe
VIDEO_PATH = "sample.mp4"

def read_file(file_path: str) -> str:
    """Read a file and return as base64 encoded string."""
    try:
        with open(file_path, 'rb') as file:
            file_content = file.read()
            return base64.b64encode(file_content).decode('utf-8')
    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 = {
    "inputPrompt": "tell me about the video",
    "mediaSource": {
        "base64String": read_file(VIDEO_PATH)
    }
}

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

response_body = json.loads(response['body'].read())
print(json.dumps(response_body, indent=2))

The TwelveLabs Marengo Embed 2.7 model generates vector embeddings from video, text, audio, or image inputs. These embeddings can be used for similarity search, clustering, and other machine learning (ML) tasks. The model supports asynchronous inference through the Bedrock StartAsyncInvoke API.

For video source, you can request JSON format for the TwelveLabs Marengo Embed 2.7 model using the StartAsyncInvoke API.

{
    "modelId": "twelvelabs.marengo-embed-2-7-v1:0",
    "modelInput": {
        "inputType": "video",
        "mediaSource": {
            "s3Location": {
                "uri": "s3://your-video-object-s3-path",
                "bucketOwner": "your-video-object-s3-bucket-owner-account"
            }
        }
    },
    "outputDataConfig": {
        "s3OutputDataConfig": {
            "s3Uri": "s3://your-bucket-name"
        }
    }
}

You can get a response delivered to the specified S3 location.

{
    "embedding": [0.345, -0.678, 0.901, ...],
    "embeddingOption": "visual-text",
    "startSec": 0.0,
    "endSec": 5.0
}

To help you get started, check out a broad range of code examples for multiple use cases and a variety of programming languages. To learn more, visit TwelveLabs Pegasus 1.2 and TwelveLabs Marengo Embed 2.7 in the AWS Documentation.

Now available
TwelveLabs models are generally available today in Amazon Bedrock: the Marengo model in the US East (N. Virginia), Europe (Ireland), and Asia Pacific (Seoul) Region, and the Pegasus model in US West (Oregon), and Europe (Ireland) Region accessible with cross-Region inference from US and Europe Regions. Check the full Region list for future updates. To learn more, visit the TwelveLabs in Amazon Bedrock product page and the Amazon Bedrock pricing page.

Give TwelveLabs models a try on the Amazon Bedrock console today, and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

— Channy

Updated on July 16, 2025 – Revised the screenshots and code part.

The post TwelveLabs video understanding models are now available in Amazon Bedrock appeared first on The NOKIA Blog.

Today, we’re announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P6e-GB200 UltraServers, accelerated by NVIDIA GB200 NVL72 to offer the highest GPU performance for AI training and inference. Amazon EC2 UltraServers connect multiple EC2 instances using a dedicated, high-bandwidth, and low-latency accelerator interconnect across these instances.

The NVIDIA Grace Blackwell Superchips connect two high-performance NVIDIA Blackwell tensor core GPUs and an NVIDIA Grace CPU based on Arm architecture using the NVIDIA NVLink-C2C interconnect. Each Grace Blackwell Superchip delivers 10 petaflops of FP8 compute (without sparsity) and up to 372 GB HBM3e memory. With the superchip architecture, GPU and CPU are colocated within one compute module, increasing bandwidth between GPU and CPU significantly compared to current generation EC2 P5en instances.

With EC2 P6e-GB200 UltraServers, you can access up to 72 NVIDIA Blackwell GPUs within one NVLink domain to use 360 petaflops of FP8 compute (without sparsity) and 13.4 TB of total high bandwidth memory (HBM3e). Powered by the AWS Nitro System, P6e-GB200 UltraServers are deployed in EC2 UltraClusters to securely and reliably scale to tens of thousands of GPUs.

EC2 P6e-GB200 UltraServers deliver up to 28.8 Tbps of total Elastic Fabric Adapter (EFAv4) networking. EFA is also coupled with NVIDIA GPUDirect RDMA to enable low-latency GPU-to-GPU communication between servers with operating system bypass.

EC2 P6e-GB200 UltraServers specifications
EC2 P6e-GB200 UltraServers are available in sizes ranging from 36 to 72 GPUs under NVLink. Here are the specs for EC2 P6e-GB200 UltraServers:

P6e-GB200 UltraServers are ideal for the most compute and memory intensive AI workloads, such as training and inference of frontier models, including mixture of experts models and reasoning models, at the trillion-parameter scale.

You can build agentic and generative AI applications, including question answering, code generation, video and image generation, speech recognition, and more.

P6e-GB200 UltraServers in action
You can use EC2 P6e-GB200 UltraServers in the Dallas Local Zone through EC2 Capacity Blocks for ML. The Dallas Local Zone (us-east-1-dfw-2a) is an extension of the US East (N. Virginia) Region.

To reserve your EC2 Capacity Blocks, choose Capacity Reservations on the Amazon EC2 console. You can select Purchase Capacity Blocks for ML and then choose your total capacity and specify how long you need the EC2 Capacity Block for u-p6e-gb200x36 or u-p6e-gb200x72 UltraServers.

Once Capacity Block is successfully scheduled, it is charged up front and its price doesn’t change after purchase. The payment will be billed to your account within 12 hours after you purchase the EC2 Capacity Blocks. To learn more, visit Capacity Blocks for ML in the Amazon EC2 User Guide.

To run instances within your purchased Capacity Block, you can use AWS Management Console, AWS Command Line Interface (AWS CLI) or AWS SDKs. On the software side, you can start with the AWS Deep Learning AMIs. These images are preconfigured with the frameworks and tools that you probably already know and use: PyTorch, JAX, and a lot more.

You can also integrate EC2 P6e-GB200 UltraServers seamlessly with various AWS managed services. For example:

• Amazon SageMaker Hyperpod provides managed, resilient infrastructure that automatically handles the provisioning and management of P6e-GB200 UltraServers, replacing faulty instances with preconfigured spare capacity within the same NVLink domain to maintain performance.
• Amazon Elastic Kubernetes Services (Amazon EKS) allows one managed node group to span across multiple P6e-GB200 UltraServers as nodes, automating their provisioning and lifecycle management within Kubernetes clusters. You can use EKS topology-aware routing for P6e-GB200 UltraServers, enabling optimal placement of tightly coupled components of distributed workloads within a single UltraServer’s NVLink-connected instances.
• Amazon FSx for Lustre file systems provide data access for P6e-GB200 UltraServers at the hundreds of GB/s of throughput and millions of input/output operations per second (IOPS) required for large-scale HPC and AI workloads. For fast access to large datasets, you can use up to 405 TB of local NVMe SSD storage or virtually unlimited cost-effective storage with Amazon Simple Storage Service (Amazon S3).

Now available
Amazon EC2 P6e-GB200 UltraServers are available today in the Dallas Local Zone (us-east-1-dfw-2a) through EC2 Capacity Blocks for ML. For more information, visit the Amazon EC2 pricing page.

Give Amazon EC2 P6e-GB200 UltraServers a try in the Amazon EC2 console. To learn more, visit the Amazon EC2 P6e instances page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Channy

The post New Amazon EC2 P6e-GB200 UltraServers accelerated by NVIDIA Grace Blackwell GPUs for the highest AI performance appeared first on The NOKIA Blog.

Today, we’re announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P6-B200 instances powered by NVIDIA B200 to address customer needs for high performance and scalability in artificial intelligence (AI), machine learning (ML), and high performance computing (HPC) applications.

Amazon EC2 P6-B200 instances accelerate a broad range of GPU-enabled workloads but are especially well-suited for large-scale distributed AI training and inferencing for foundation models (FMs) with reinforcement learning (RL) and distillation, multimodal training and inference, and HPC applications such as climate modeling, drug discovery, seismic analysis, and insurance risk modeling.

When combined with Elastic Fabric Adapter (EFAv4) networking, hyperscale clustering by EC2 UltraClusters, and advanced virtualization and security capabilities by AWS Nitro System, you can train and serve FMs with increased speed, scale, and security. These instances also deliver up to two times the performance for AI training (time to train) and inference (tokens/sec) compared to EC2 P5en instances.

You can accelerate time-to-market for training FMs and deliver faster inference throughput, which lowers inference cost and helps increase adoption of generative AI applications as well as increased processing performance for HPC applications.

EC2 P6-B200 instances specifications
New EC2 P6-B200 instances provide eight NVIDIA B200 GPUs with 1440 GB of high bandwidth GPU memory, 5th Generation Intel Xeon Scalable processors (Emerald Rapids), 2 TiB of system memory, and 30 TB of local NVMe storage.

Here are the specs for EC2 P6-B200 instances:

These instances feature up to 125 percent improvement in GPU TFLOPs, 27 percent increase in GPU memory size, and 60 percent increase in GPU memory bandwidth compared to P5en instances.

P6-B200 instances in action
You can use P6-B200 instances in the US West (Oregon) AWS Region through EC2 Capacity Blocks for ML. To reserve your EC2 Capacity Blocks, choose Capacity Reservations on the Amazon EC2 console.

Select Purchase Capacity Blocks for ML and then choose your total capacity and specify how long you need the EC2 Capacity Block for p6-b200.48xlarge instances. The total number of days that you can reserve EC2 Capacity Blocks is 1-14 days, 21 days, 28 days, or multiples of 7 up to 182 days. You can choose your earliest start date for up to 8 weeks in advance.

Now, your EC2 Capacity Block will be scheduled successfully. The total price of an EC2 Capacity Block is charged up front, and the price doesn’t change after purchase. The payment will be billed to your account within 12 hours after you purchase the EC2 Capacity Blocks. To learn more, visit Capacity Blocks for ML in the Amazon EC2 User Guide.

When launching P6-B200 instances, you can use AWS Deep Learning AMIs (DLAMI) to support EC2 P6-B200 instances. DLAMI provides ML practitioners and researchers with the infrastructure and tools to quickly build scalable, secure, distributed ML applications in preconfigured environments.

To run instances, you can use AWS Management Console, AWS Command Line Interface (AWS CLI) or AWS SDKs.

You can integrate EC2 P6-B200 instances seamlessly with various AWS managed services such as Amazon Elastic Kubernetes Services (Amazon EKS), Amazon Simple Storage Service (Amazon S3), and Amazon FSx for Lustre. Support for Amazon SageMaker HyperPod is also coming soon.

Now available
Amazon EC2 P6-B200 instances are available today in the US West (Oregon) Region and can be purchased as EC2 Capacity blocks for ML.

Give Amazon EC2 P6-B200 instances a try in the Amazon EC2 console. To learn more, refer to the Amazon EC2 P6 instance page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Channy

How is the News Blog doing? Take this 1 minute survey!

(This survey is hosted by an external company. AWS handles your information as described in the AWS Privacy Notice. AWS will own the data gathered via this survey and will not share the information collected with survey respondents.)

The post New Amazon EC2 P6-B200 instances powered by NVIDIA Blackwell GPUs to accelerate AI innovations appeared first on The NOKIA Blog.

The Amazon Web Services (AWS) Summit 2025 season launched this week, starting with the Paris Summit. These free events bring together the global cloud computing community for learning and collaboration. AWS Community Day Romania, held on April 11th, showcased how the local community creates opportunities for collective growth and inclusion.

Last week’s launches
Announcing up to 85% price reductions for Amazon S3 Express One Zone — S3 Express One Zone, a high-performance storage class, now has reduced storage prices by 31 percent, PUT request prices by 55 percent, and GET request prices by 85 percent. In addition, S3 Express One Zone has reduced the per-GB charges for data uploads and retrievals by 60 percent. These charges now apply to all bytes transferred rather than just portions of requests greater than 512 KB.

Here is a price reduction table in the US East (N. Virginia) AWS Region:

AWS announces Pixtral Large 25.02 model in Amazon Bedrock serverless — The Pixtral Large 25.02, developed by Mistral AI, combines advanced vision and language understanding, boasting a 128K context window and multilingual capabilities. This agent-centric design simplifies integration with existing systems. Prompt adherence improves reliability when working with Retrieval Augmented Generation (RAG) applications and large context scenarios.

Introducing Amazon Nova Sonic: Human-like voice conversations for generative AI applications — Amazon Nova Sonic, the newest addition to the Amazon Nova family of foundation models (FMs) is available in Amazon Bedrock to create human-like voice conversations for applications. It unifies speech and text processing into one model, reducing complexity and enhancing natural interactions. Start today with the Amazon Nova model cookbook repository.

Amazon Bedrock Guardrails enhances generative AI application safety with new capabilities — Amazon Bedrock Guardrails introduces new capabilities to enhance generative AI application safety, including multimodal toxicity detection, enhanced Personally Identifiable Information (PII) protection, AWS Identity and Access Management (AWS IAM) policy enforcement, selective guardrail application, and monitor mode for pre-deployment analysis.

AWS App Studio introduces a prebuilt solutions catalog and cross-instance Import and Export — This is a prebuilt solutions catalog with ready-to-use applications and patterns and cross-instance Import and Export functionality. These features help you streamline development applications, reducing setup time to under 15 minutes. Learn more about this in AWS App Studio introduces a prebuilt solutions catalog and cross-instance Import and Export blog.

Amazon Nova Reel 1.1: Featuring up to 2-minutes multi-shot videos — Amazon Nova Reel 1.1 enhances video generation through Amazon Bedrock with support for 2-minute multi-shot videos. You can now create content using either single prompts for automatic generation or custom prompts for individual shots, offering flexible options for marketing and social media content creation.

AWS IAM Identity Center now offers improved error messages and AWS CloudTrail logging for provisioning issues — AWS Identity and Access Management (IAM) Identity Center has enhanced its service with improved error messages and AWS CloudTrail logging capabilities. These updates help users better troubleshoot synchronization issues when managing workforce identities across AWS accounts and applications, while enabling automated monitoring and auditing of provisioning problems.

AWS WAF Console adds new top insights visualizations in additional regions — AWS WAF Console now offers enhanced traffic visualization features in AWS GovCloud (US) Regions. The all traffic dashboard includes new top insights based on Amazon CloudWatch logs, helping customers analyze traffic patterns, identify security threats, and optimize WAF configurations through detailed metrics.

AWS Step Functions expands data source and output options for Distributed Map — AWS Step Functions enhances Distributed Map with expanded data source support, including JSONL and various delimited file formats from Amazon Simple Storage Service (Amazon S3). The update also adds new output transformation options, enabling more flexible parallel processing workflows and better integration with downstream systems.

Amazon CloudWatch now provides lock contention diagnostics for Aurora PostgreSQL — Amazon CloudWatch Database Insights introduces lock contention diagnostics for Amazon Aurora PostgreSQL in Advanced mode. The feature visualizes blocking and waiting sessions, helping users identify root causes of lock contention issues, with 15-month historical data retention for comprehensive troubleshooting.

Get updated with all the announcements of AWS announcements on the What’s New with AWS? page.

Other AWS blog posts
Reduce ML training costs with Amazon SageMaker HyperPod — Amazon SageMaker HyperPod addresses hardware failures in large-scale Machine Learning (ML) model training by automatically detecting and replacing faulty instances. The solution reduces downtime from 280 to 40 minutes per failure, potentially saving 32% of training time for large clusters. For a 10-million GPU-hour training job, this translates to $25.6M in cost savings.

Model customization, RAG, or both: A case study with Amazon Nova — A study comparing model customization with fine-tuning and Retrieval Augmented Generation (RAG) approaches with Amazon Nova models. Key findings show combining both methods yields best results: RAG works well for dynamic data and domain insights, while fine-tuning excels in specialized tasks and latency reduction.

Generate user-personalized communication with Amazon Personalize and Amazon Bedrock — Amazon Personalize and Amazon Bedrock work together to create personalized marketing emails. Learn how to create personalized user communications by combining Amazon Personalize for movie recommendations with Amazon Bedrock for generating tailored email content based on user preferences and demographics.

Implement human-in-the-loop confirmation with Amazon Bedrock Agents — When implementing human validation in Amazon Bedrock Agents, developers have two primary frameworks at their disposal: user confirmation and return of control (ROC). Using an HR application example, user confirmation allows simple yes/no validation before executing actions, while ROC enables users to modify parameters before execution.

Multi-LLM routing strategies for generative AI applications on AWS — Learn how to implement multi-Large Language Model (LLM) routing strategies for AWS generative AI applications using static routing, dynamic routing with Amazon Bedrock, or custom solutions for optimal model selection and cost efficiency.

Here are my personal favorites posts from community.aws:

Building a RAG System for Video Content Search and Analysis — In this blog, I’ll show you how to build a RAG system that makes video content searchable and analyzable. Unlocking video content has never been more crucial in today’s digital landscape. Whether you’re managing educational materials, corporate training, or entertainment content, the ability to search and analyze video content efficiently can transform how we interact with multimedia resources.

Build Serverless GenAI Apps Faster with Amazon Q Developer CLI Agent — Amazon Q Developer CLI Agent enables rapid serverless GenAI app development. With one prompt, it generates infrastructure code, Lambda functions, and integrates with Claude 3 Haiku on Amazon Bedrock.

Speech-to-Speech AI: From Dr. Sbaitso to Amazon Nova Sonic — The evolution of speech-to-speech AI, from Dr. Sbaitso (1990s) to Amazon Nova Sonic. New AWS service enables real-time bidirectional conversations through Amazon Bedrock for more natural applications.

Setup Model Context Protocol (MCP) using Amazon Bedrock — A guide to setting up Model Context Protocol (MCP) desktop client with Amazon Bedrock models, enabling seamless integration between AI applications and external tools using Goose client.

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

AWS GenAI Lofts — GenAI Lofts available around the world, offer collaborative spaces and immersive experiences for startups and developers. You can join in-person GenAI Loft San Francisco events such as GenAI in EdTech: A Hands-On Workshop (April 15), and Unstructured Data Meetup SF (April 16). Find your nearest event at GenAI Lofts.

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: Amsterdam (April 16), London (April 30), and Poland (May 5).

AWS re:Inforce — AWS re:Inforce (June 16–18) in Philadelphia, PA, is our annual learning event devoted to all things AWS cloud security. Registration is open. Be ready to join more than 5,000 security builders and leaders.

AWS Community Days — Join community-led conferences featuring technical discussions, workshops, and hands-on labs driven by expert AWS users and industry leaders from around the world. Upcoming AWS Community Days are scheduled for April 19 in Turkey, and on April 29 in Prague with Jeff Barr as Opening Keynote Speaker.

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

Create your AWS Builder ID and reserve your alias. Builder ID is a universal login credential that gives you access—beyond the AWS Management Console—to AWS tools and resources, including over 600 free training courses, community features, and developer tools such as Amazon Q Developer.

That’s all for this week. Stay tuned for next week’s Weekly Roundup!

— Eli

Thanks to Andra Somesan for the AWS Community Romania photo and Thembile Martis for the AWS Paris Summit photo.

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

How is the News Blog doing? Take this 1 minute survey!

(This survey is hosted by an external company. AWS handles your information as described in the AWS Privacy Notice. AWS will own the data gathered via this survey and will not share the information collected with survey respondents.)

The post AWS Weekly Review: Amazon S3 Express One Zone price cuts, Pixtral Large on Amazon Bedrock, Amazon Nova Sonic, and more (April 14, 2025) 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.