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AWS Lambda now supports function-level configuration for recursive loop detection

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AWS Lambda now supports function-level configuration which allows you to disable or enable recursive loop detection. Lambda recursive loop detection, which is enabled by default, is a preventative guardrail that automatically detects and stops recursive invocations between Lambda and other supported services, preventing runaway workloads.

Before, customers running intentionally recursive patterns could only turn off recursive loop detection on a per-account basis through AWS Support. Now customers can disable or enable recursive loop detection on a per-function basis, allowing them to run their intentionally recursive workflows while protecting the remaining functions in their account from runaway workloads caused by unintended recursive invocations.

These new API actions are available in all AWS Regions where recursive loop detection is supported. You can set your function’s recursion configuration programmatically, as a parameter in your CloudFormation template, or within the AWS Lambda Console.

To learn more about Lambda’s new recursive loop detection API actions, please refer to Lambda’s API reference or the launch blog post .
 

Amazon Connect in-app, web, and video calling is now available in Africa (Cape Town) region

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Amazon Connect now provides in-app and web voice and video calling capabilities in Africa (Cape Town) region, making it easier to deliver more personalized voice and video experiences in your websites and mobile applications. These voice and video capabilities allow customers to contact you without having to leave your website or mobile application. You can use these capabilities to pass contextual information to Amazon Connect, enabling you to personalize the customer experience based on attributes such as the customer’s profile, authentication status, or actions previously taken within the app.
Using the fully managed communication widget, you can implement these new voice and video calling capabilities with as little as a single line of code. You can also create a fully custom experience for your customers by leveraging the SDK. In addition, you can use the same configuration, routing, analytics, and agent application as with telephone calls and chats, helping saving costly integration time, license fees, and maintenance expenses.

To learn more and get started, please refer to the help documentation or visit the Amazon Connect website. To learn more about pricing, please visit the Amazon Connect pricing page .
 

Amazon S3 now supports conditional writes

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Amazon S3 adds support for conditional writes that can check for the existence of an object before creating it. This capability can help you more easily prevent applications from overwriting any existing objects when uploading data. You can perform conditional writes using PutObject or CompleteMultipartUpload API requests in both general purpose and directory buckets.

Using conditional writes, you can simplify how distributed applications with multiple clients concurrently update data in parallel across shared datasets. Each client can conditionally write objects, making sure that it does not overwrite any objects already written by another client. This means you no longer need to build any client-side consensus mechanisms to coordinate updates or use additional API requests to check for the presence of an object before uploading data. Instead, you can reliably offload such validations to S3, enabling better performance and efficiency for large-scale analytics, distributed machine learning, and other highly parallelized workloads. To use conditional writes, you can add the HTTP if-none-match conditional header along with PutObject and CompleteMultipartUpload API requests.

This feature is available at no additional charge in all AWS Regions, including the AWS GovCloud (US) Regions and the AWS China Regions. You can use the AWS SDK, API, or CLI to perform conditional writes. To learn more about conditional writes, visit the S3 User Guide .
 

Amazon SageMaker Canvas now supports data flows import, and faster data prep for ML

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Amazon SageMaker Data Wrangler in Amazon SageMaker Canvas now supports importing data flows from Amazon SageMaker Studio Classic, as well as faster and more flexible data preparation for machine learning (ML). With the latest version of SageMaker Data Wrangler in SageMaker Canvas, you can now import data from S3 more easily with custom delimiters and more sampling options, and prepare data with improved performance. In addition, you can validate transforms faster, and easily iterate on the data recipes. You can also import data flows from SageMaker Studio Classic to take advantage of the latest data preparation features and enhancements in SageMaker Canvas.

Aggregating, analyzing, and transforming large amounts of data is the most time-consuming part of an ML project because it is a highly iterative and repetitive process. With these new enhancements, you can import data with different sampling methods such as top-k, random or stratified, and adjust the sample size and method as needed to get a representative sample. You can transform data with lower latency, quickly validate the impact of transforms on the data size, and reorder the steps as needed. In addition, you can copy a data recipe and replace the data sources to reuse it for different datasets and models. Last but not least, you can one-click import all the existing data flows from SageMaker Data Wrangler in SageMaker Studio Classic to SageMaker Canvas, or manually import specific data flows through S3 or local file uploads.

These enhanced data preparation capabilities are available all AWS regions where SageMaker Canvas is supported. For more information, see the blog and the AWS technical documentation .

AWS CloudHSM launches new hsm2m.medium instance type

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AWS CloudHSM launched hsm2m.medium with support for Federal Information Processing Standard (FIPS) 140-3 Level 3 and non-FIPS CloudHSM clusters.

The new instance type provides customers with new features, increased performance, and flexibility. hsm2m.medium offers increased key capacity and higher elliptic curve performance compared with the existing hsm1.medium instance type. Backups from CloudHSM clusters based on hsm1.medium are compatible with hsm2m.medium. hsm2m.medium supports Mutual Transport Layer Security (mTLS) for communication from the CloudHSM client SDKs to the CloudHSM cluster.

The new hsm2m.medium instance type is available in four regions – United States (N. Virginia, Ohio, Oregon) and Europe (Dublin).

To learn more about the AWS CloudHSM cluster modes and HSM types, see the AWS CloudHSM user guide, and visit the AWS CloudHSM page for pricing details. For availability in additional regions, contact support.

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