Cloud Computing Köln

Amazon SageMaker Unified Studio Notebooks now support EMR Serverless

Amazon SageMaker Unified Studio Notebooks now support Amazon EMR Serverless with Apache Spark Connect, giving data engineers and analysts more flexibility in choosing their Spark runtime for interactive analytics and data engineering workloads. In addition to Amazon Athena Spark, users can now leverage Amazon EMR Serverless as their Spark runtime, selecting the optimal engine based on their requirements.
With this launch, you can run PySpark and Spark SQL on an EMR Serverless Spark Application in Notebook cells. Users can select their Spark runtime from the Notebook side panel, and the selected runtime applies to both Python and SQL cells. Additionally, users can leverage SageMaker Data Agent, the built-in AI assistant, to generate code and execution plans from natural language prompts, accelerating Spark development workflows with EMR Serverless. Organizations can leverage pre-initialized capacity to improve session start times, while benefiting from unified Spark UI monitoring across all supported engines for consistent visibility into job execution and performance. Additionally, EMR Serverless provides VPC connectivity support for workloads requiring network isolation.
This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available, supporting both SageMaker Unified Studio notebooks and JupyterLab IDE environments. To get started, see Amazon SageMaker Unified Studio User Guide.
Quelle: aws.amazon.com

Run Interactive Workloads on Amazon EMR Serverless with Spark Connect

Amazon EMR Serverless now supports interactive sessions with Spark Connect, enabling you to develop and run Apache Spark applications from managed notebooks in Amazon SageMaker Unified Studio, as well as your favorite notebook environments and IDEs such as Jupyter and Visual Studio Code. You can also monitor and debug active and completed sessions in the EMR console, and get granular cost and usage visibility for individual sessions. 
 
An interactive session provides a persistent Spark context that seamlessly spans across cells and scripts, enabling you to blend local Python code execution with remote Spark operations within a unified environment. This is enabled by Spark Connect’s client-server architecture, which decouples your application client from the Spark driver and allows you to maintain your preferred development environment and tooling while Spark infrastructure runs independently on EMR Serverless. This architecture unlocks workflows including ad hoc data exploration, iterative step-by-step debugging, and incremental PySpark job development before deploying to production.  For observability, you get real-time session monitoring via the Spark UI, history tracking through the Spark History Server, and session management from the EMR console or API/CLI/SDK.
 
Spark Connect on Amazon EMR Serverless is available with EMR release 7.13 in all AWS Regions where Amazon EMR Serverless is available. The SageMaker Unified Studio experience is available in supported regions. To get started, visit the EMR Serverless Interactive Sessions User Guide or the Amazon SageMaker Unified Studio Getting Started guide.
Quelle: aws.amazon.com

AWS announces Claude Fable 5, the first generally available Mythos-class model

Claude Fable 5 is generally available on AWS and makes Mythos-level capabilities available to all customers, with strong safeguards designed to make it safe for broader use. Fable 5 is state-of-the-art on nearly all tested benchmarks and delivers a step-change in autonomous knowledge work and coding for developers and enterprises building production AI applications. Claude Mythos 5, the same model without those safety classifiers, is available to a small group of customers who currently have access to Claude Mythos Preview.
Claude Fable 5 can run for extended periods on complex knowledge work and coding tasks without intervention, representing a fundamental shift in the types of problems customers can solve with AI. It is built for professional tasks in finance, legal, marketing, sales, data, and engineering — proactively self-updating skills based on learnings, developing its own evaluation harnesses, and verifying its work before delivery. 
Customers have two ways to access Claude Fable 5: Amazon Bedrock and Claude Platform on AWS. Amazon Bedrock keeps your data within AWS infrastructure and provides access to Claude Fable 5 through a unified service with AWS-managed features like Guardrails, Knowledge Bases, and regional data residency. To learn more, see Amazon Bedrock documentation and regional availability. 
Claude Platform on AWS, operated by Anthropic, gives you direct access to Anthropic’s native Claude platform experience with unified AWS billing and authentication. To get started, see the Claude Platform on AWS documentation.
Quelle: aws.amazon.com

AWS FinOps Agent is now available in preview

Today, AWS announces the preview of AWS FinOps Agent, a frontier agent for FinOps practitioners and engineering teams that answers cost questions, surfaces optimization opportunities, automatically investigates cost anomalies, and runs recurring FinOps workflows on a schedule you define.
With the AWS FinOps Agent, you can ask questions about your AWS costs and generate cloud cost reports for finance and engineering teams. The agent surfaces rightsizing, idle resource, and Savings Plans recommendations from AWS Cost Optimization Hub and AWS Compute Optimizer, and can open Jira tickets on your behalf. When a cost anomaly is detected, FinOps Agent can automatically investigate the root cause and can post the findings to a Slack channel, so engineering teams are notified without manual triage.
AWS FinOps Agent (preview) is available in the US East (N. Virginia) Region and includes cost and usage data covering all AWS Regions, except AWS GovCloud (US) Regions and AWS China (Beijing and Ningxia) Regions. AWS FinOps Agent is offered at no additional charge during the preview.
Learn more about AWS FinOps Agent in the User Guide, product details page, and the blog. Get started by visiting the AWS FinOps Agent page in the AWS Management Console.
Quelle: aws.amazon.com