10/21/2022, 12:00:00 AM ~ 10/24/2022, 12:00:00 AM (UTC)
Recent Announcements
AWS announces Amazon EKS Anywhere on Apache CloudStack
We are excited to announce the general availability of Amazon Elastic Kubernetes Service (Amazon EKS) Anywhere on Apache CloudStack which expands the choice of infrastructure options for customers running Kubernetes on-premises. Apache CloudStack enhances the list of deployment options for Amazon EKS Anywhere customers, which already includes bare metal servers and VMware vSphere.
AWS Nitro Enclaves is now supported on AWS Graviton
Starting today, AWS Nitro Enclaves is available on AWS Graviton2 and AWS Graviton3 Amazon Elastic Compute Cloud (EC2) instances. With this launch, Nitro Enclaves is supported on the majority of Graviton, Intel, and AMD-based Amazon EC2 instance types built on the AWS Nitro System.
AWS Global Accelerator announces AddEndpoint and RemoveEndpoint APIs
AWS Global Accelerator now offers two new APIs, AddEndpoint and RemoveEndpoint, that allow you to add and remove endpoints behind your accelerator. With these new APIs, you can now configure endpoints behind your accelerators without having to provide the full list of endpoints for adding or removing endpoints. Both AddEndpoint and RemoveEndpoint APIs can accommodate up to 10 endpoints in a single API call. The new APIs help increase scalability and reduce errors when you manage your endpoint workflows with Global Accelerator. You can continue to use the AddEndpointGroup and RemoveEndpointGroup APIs to add and remove endpoint groups, and the DescribeEndpointGroup API to describe all endpoints behind an accelerator.
Announcing dark mode support in the AWS Management Console
Today, we are excited to launch dark mode as a beta feature in the AWS Management Console. Dark mode is available as a setting for visual mode in Unified Settings. The setting persists for customers across browsers and devices.
Amazon EKS Anywhere now includes support for Red Hat Enterprise Linux
Today, we are excited to announce support for Red Hat Enterprise Linux (RHEL) in Amazon EKS Anywhere. In addition to Bottlerocket and Ubuntu, you now have broader choice of operating systems to create and operate Amazon EKS Anywhere clusters with RHEL in your on-premises data centers. RHEL support is available for Amazon EKS Anywhere clusters running on VMware vSphere, on Apache CloudStack, or directly on bare metal servers.
Today, we are excited to announce the ability to dynamically support different datasets stored on S3 through use of parameters in Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. With Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from a single visual interface. Previously, customers did not have an easy way to dynamically refer to data sets when running Data Wrangler processing jobs on a schedule. Customers also lacked a way to more easily filter down files in an S3 bucket to be used for processing. Finally, customers lacked a simple way to change data sources when running a Data Wrangler processing job from the Create Job workflow or from a Data Wrangler processing notebook.
Schedule data preparation jobs with Amazon SageMaker Data Wrangler
Today, we are excited to announce support for scheduling Data Wrangler processing jobs in Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from a single visual interface. Previously, scheduling a data processing job would involve integrating with a serverless compute capability and an event bus service. This process would also involve writing code to schedule the data processing job in a production environment. Integrating these various capabilities together and writing the code to orchestrate this workflow can be a laborious, time-consuming task for data scientists, data engineers and ML engineers.
Amazon S3 on Outposts now supports Access Point aliases to simplify application access to data
Amazon S3 on Outposts now supports Access Point aliases to simplify application access to data. Beginning today, you can configure applications to use the Access Point alias in place of the Amazon Resource Name (ARN) when accessing S3 on Outposts buckets. With Access Points, you can create hundreds of unique policies to control access to shared datasets, and applications can access S3 on Outposts buckets by utilizing this alias. Amazon S3 on Outposts Access Point aliases are now available in all AWS Regions where AWS Outposts are available at no additional cost.
Reduce dimensionality using PCA in Amazon SageMaker Data Wrangler
Today, we are excited to announce support for dimensionality reduction using principal components analysis (PCA) in Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. With Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from a single visual interface. PCA is a popular technique for analyzing large datasets containing a high number of dimensions per observation and is a helpful statistical technique for reducing the dimensionality of a dataset for use with popular ML algorithms like XgBoost and random forest. Previously, to perform PCA on a data set, data scientists would have to find appropriate libraries and write code to reduce high-dimensional data.
AWS CloudTrail Lake now supports export of signed query results to Amazon S3
AWS CloudTrail Lake now allows you to export signed query results from Lake to a specified Amazon S3 bucket of your choice. This feature lets you integrate CloudTrail Lake with your downstream workflows for further analysis and visualization purposes, such as query join operations with data sets in Amazon Athena, and dashboards with Amazon QuickSight. Every query result file exported from CloudTrail Lake includes a CloudTrail signature. This signature file, designed to provide security assurance, can help verify any unauthorized modifications to the data exported from CloudTrail Lake.
Amazon FSx for Windows File Server is now available in the AWS Asia Pacific (Jakarta) Region
Customers in the AWS Asia Pacific (Jakarta) Region can now use Amazon FSx for Windows File Server.
YouTube
AWS Black Belt Online Seminar (Japanese)
AWS Blogs
AWS Japan Blog (Japanese)
- Large-scale Bayesian machine learning model training solution using AWS Batch
- [Event Report] 1st Metaverse x AWS Metaverse Frontline Customer Stories
- [Event Report] “AWS Autumn Amazon EC2 Deep Dive Festival 2022” Seminar
- Accelerating Digital Transformation Toward Better Livelihoods and Regional Economic Revitalization
- [Event Report] AWS Media Seminar 2022 Q3 ~ Customer ID Management and Data Utilization Realized by AWS: CIAM on AWS ~
AWS Japan Startup Blog (Japanese)
- Everyone can easily achieve advanced data analysis — datagusto’s AWS use case
- A must-see for startups aiming to go public! “Security Measures and Internal Control (IT Control) that Technical Departments Should Address in Preparation for Listing” - AWS Startup.fm