Optimize AWS administration with IAM paths

TutoSartup excerpt from this article:
As organizations expand their Amazon Web Services (AWS) environment and migrate workloads to the cloud, they find themselves dealing with many AWS Identity and Access Management (IAM) roles and policies... These roles and policies multiply because IAM fills a crucial role in securing and controlling access to AWS resources... With additional workloads and new data access patterns, the number of IA...

Schedule Amazon SageMaker notebook jobs and manage multi-step notebook workflows using APIs

TutoSartup excerpt from this article:
Amazon SageMaker notebook jobs allow data scientists to run their notebooks on demand or on a schedule with a few clicks in SageMaker Studio... With this launch, you can programmatically run notebooks as jobs using APIs provided by Amazon SageMaker Pipelines, the ML workflow orchestration feature of Amazon SageMaker... Furthermore, you can create a multi-step ML workflow with multiple dependent n...

Package and deploy models faster with new tools and guided workflows in Amazon SageMaker

TutoSartup excerpt from this article:
I’m happy to share that Amazon SageMaker now comes with an improved model deployment experience to help you deploy traditional machine learning (ML) models and foundation models (FMs) faster... As a data scientist or ML practitioner, you can now use the new ModelBuilder class in the SageMaker Python SDK to package models, perform local inference to validate runtime errors, and deploy to SageMak...

Use natural language to explore and prepare data with a new capability of Amazon SageMaker Canvas

TutoSartup excerpt from this article:
Today, I’m happy to introduce the ability to use natural language instructions in Amazon SageMaker Canvas to explore, visualize, and transform data for machine learning (ML)... SageMaker Canvas now supports using foundation model-(FM) powered natural language instructions to complement its comprehensive data preparation capabilities for data exploration, analysis, visualization, and transformat...

Amazon SageMaker adds new inference capabilities to help reduce foundation model deployment costs and latency

TutoSartup excerpt from this article:
With the new inference capabilities, you can deploy one or more foundation models (FMs) on the same SageMaker endpoint and control how many accelerators and how much memory is reserved for each FM... This helps to improve resource utilization, reduce model deployment costs on average by 50 percent, and lets you scale endpoints together with your use cases... For each FM, you can define separate ...

Announcing new tools and capabilities to enable responsible AI innovation

TutoSartup excerpt from this article:
Over the past year, we have introduced new capabilities in our generative AI applications and models such as built-in security scanning in Amazon CodeWhisperer, training to detect and block harmful content in Amazon Titan, and data privacy protections in Amazon Bedrock... And we continue to work hand-in-hand with customers to operationalize responsible AI with purpose-built tools like Amazon Sag...