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.

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.

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.

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.

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.

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.

There was once a guy who said he felt he had been “put out to pasture” at the age of 52. He had been running TI’s semiconductor division but was then transferred to the company’s less than successful consumer business, and later to a staff job. He then founded a company which, last year, had …

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Cree is aiming at premium indoor and outdoor lighting with a family of metal-substrate CoBs called CMB, tuned as performance upgrades from its CXA, CXB and CMA families, while retaining mechanical and optical compatibility. “Up to 8% improvement in lumens/W vs previous generations by leveraging the latest technology platform,” is the company’s claim. They are …

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What caught the eye of David Manners this week, for example, was Apple dropping its plan to buy Yangtze NAND…

The post What caught your eye this week? (Yangtze NAND, Quadrature encoders, Inmarsat acquisition) appeared first on Electronics Weekly .

A LIN pre-driver IC for relay DC motors from Melexis  includes 48 KB of memory (16 KB ROM for the included LIN protocol and 32 KB Flash for the application software. The MLX81160 is the latest addition to the company’s Gen3 family of compatible embedded motor drivers. By leveraging high-voltage SOI technology, this LIN-based pre-driver …

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