Today, we are announcing support for Amazon Elastic Compute Cloud (EC2) Dedicated Hosts on AWS Outposts, which makes it easier for customers to bring their existing software licenses and workloads that require a dedicated physical server to their Outpost Racks. In addition, customers now have greater flexibility in instance type deployment and more granular placement control, all with consistent hybrid experience on AWS Outposts.

Amazon SageMaker JumpStart  helps you quickly and easily solve your machine learning problems with one-click access to (a) more than 300 popular model collections from TensorFlow Hub, PyTorch Hub, Hugging Face and Gluon CV, and (b) 18 end-to-end solutions that solve common business problems such as demand forecasting, fraud detection and document understanding. The available models can be used for a wide range of machine learning tasks including image classification, object detection, semantic segmentation, instance segmentation, image embedding, text classification, sentence pair classification, question answering, text embedding, text summarization, text generation, machine translation, tabular classification and tabular regression.

We are excited to announce the following price reductions for Amazon EC2 instances running SLES. When you run SLES on Amazon EC2, you are charged one combined price for the Amazon EC2 infrastructure and the SUSE OS.

AWS Storage Gateway is expanding the ways you can purchase the AWS Storage Gateway Hardware Appliance, and for the first-time enabling resellers to offer the appliance. Now you can procure the hardware appliance through the reseller of your choice, and leverage your existing purchasing agreements.

We are announcing the launch of metered billing for AWS Data Exchange for APIs, which gives data providers the ability to license APIs to subscribers on a pay-as-you-go basis. AWS Data Exchange for APIs enables customers to find, subscribe to, and use third-party API products listed on AWS Data Exchange. This new feature allows data subscribers to subscribe to APIs and only pay for what they use.

Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for you to add speech-to-text capabilities to your applications. Today, we are excited to announce automatic language identification support for multi-lingual audio in batch mode. If your audio recording contains more than one language, you can enable multi-language identification, which identifies all languages spoken in the audio file and creates transcript using each identified language. This means that if speakers change languages mid-conversation, or if each participant is speaking a different language, your transcription output detects and transcribes each language correctly. Until now, Transcribe would detect the dominant language in the audio recording and generate transcriptions in the identified language. You can now simply provide the audio files and Transcribe will detect the language from the speech signal and generate transcriptions in the identified language.

Today we are announcing the general availability of Geofences for Amplify Geo. Amplify Geo enables frontend developers to add location-aware features to their web applications. Developers looking to display geometric boundaries or Geofences on a map, can now implement a complete Geofence management solution in minutes using the cloud-connected UI widget and APIs from Amplify Geo, powered by Amazon Location Service. Geofences are geometric boundaries that can be drawn around places of interest or areas on a map.

Amazon S3 on Outposts now supports AWS PrivateLink, providing direct access to manage your S3 on Outposts storage capacity via a private endpoint within your virtual private network. This allows you to simplify your internal network architecture and perform management operations on your S3 storage by using private IP addresses in your Virtual Private Cloud (VPC), eliminating the need to use public IPs or proxy servers.

We are happy to announce the general availability of Amazon EMR Serverless, a new serverless deployment option in Amazon EMR that makes it easy and cost effective for data engineers and analysts to run petabyte-scale data analytics in the cloud. Amazon EMR is a big data solution that you can use to run large-scale distributed data processing jobs, interactive SQL queries, and machine learning (ML) applications built on open-source analytics frameworks such as Apache Spark, Apache Hive, and Presto. With EMR Serverless, you can run your Spark and Hive applications without having to configure, optimize, tune, or manage clusters.

You can now learn to use AWS Step Functions with a new workshop called The AWS Step Functions Workshop . This self-paced tutorial teaches you how to use the primary features of Step Functions through a series of interactive modules. Each module contains lesson materials you can deploy to your AWS account, covering topics such as coordinating and orchestrating application workflows, managing workflow states, creating SDK integrations with other AWS services, and more.