How to improve cross-account access for SaaS applications accessing customer accounts

TutoSartup excerpt from this article:
Several independent software vendors (ISVs) and software as a service (SaaS) providers need to access their customers’ Amazon Web Services (AWS) accounts, especially if the SaaS product accesses data from customer environments... In some cases, the providers ask the customer for an access key and a secret key, which is not recommended because these are long-term user credentials and require proc...

Fall 2023 SOC reports now available with 171 services in scope

TutoSartup excerpt from this article:
We’re proud to deliver the Fall 2023 System and Organizational (SOC) 1, 2, and 3 reports to support your confidence in AWS services... The reports cover the period October 1, 2022, to September 30, 2023... We extended the period of coverage to 12 months so that you have a full year of assurance from a single report... The SOC 2 report includes the Security, Availability, Confidentiality, and...

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...