October 2023

Posted On: Oct 19, 2023
Today we are launching Amazon SageMaker in the AWS Secret Region. Amazon SageMaker is a fully managed end-to-end machine learning service that enables data scientists, developers, and machine learning experts to quickly build, train, and host machine learning models at scale. This drastically accelerates all of your machine learning efforts and allows you to add machine learning to your production applications quickly.

• Authoring: Zero-setup hosted Jupyter notebook IDEs for data exploration, cleaning, and preprocessing. You can run these on general instance types or GPU powered instances.
• Model Training: A distributed model building, training, and validation service. You can use built-in common supervised and unsupervised learning algorithms and frameworks or create your own training with Docker containers. The training can scale to tens of instances to support faster model building. Training data is read from S3 and model artifacts are put into S3. The model artifacts are the data dependent model parameters, not the code that allows you to make inferences from your model. This separation of concerns makes it easy to deploy Amazon SageMaker trained models to other platforms.
• Model Hosting: A model hosting service with HTTPs endpoints for invoking your models to get real time inferences. These endpoints can scale to support traffic and allow you to A/B test multiple models simultaneously. Again, you can construct these endpoints using the built-in SDK or provide your own configurations with Docker images. Amazon SageMaker Neo: This allows customers to train models once, and run them anywhere with up to 7X improvement in performance. Applications running on connected devices at the edge are particularly sensitive to performance of machine learning models. They require low latency decisions, and are often deployed across a broad number of different hardware platforms.
• Amazon SageMaker Neo compiles models for specific hardware platforms, optimizing their performance automatically, allowing them to run at up to seven times the performance, without any loss in accuracy. As a result, developers no longer need to spend time hand tuning their trained models for each and every hardware platform (saving time and expense). SageMaker Neo supports hardware platforms from NVIDIA, Intel, Xilinx, Cadence, and Arm, and popular frameworks such as Tensorflow, Apache MXNet, and PyTorch.
• Amazon SageMaker GroundTruth: If you want the flexibility to build and manage your own data labeling workflows and workforce, you can use SageMaker Ground Truth. SageMaker Ground Truth is a data labeling service that makes it easy to label data and gives you the option to use third-party vendors or your own private workforce. You can also generate labeled synthetic data without manually collecting or labeling real-world data. SageMaker Ground Truth can generate hundreds of thousands of automatically labeled synthetic images on your behalf.

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Posted On: Oct 5, 2023
Today, we are announcing the general availability of Amazon WorkSpaces running WorkSpaces Streaming Protocol (WSP) in the AWS Secret Region. WSP is a cloud-native streaming protocol designed to enable your users to access a highly responsive remote desktop experience and features such as 2-way audio video and smart card support.

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Posted On: Oct 5, 2023
Starting today, the Amazon Elastic Compute Cloud (Amazon EC2) G5 instances powered by NVIDIA A10G Tensor Core GPUs are now available in the AWS Secret Region. G5 instances can be used for a wide range of graphics intensive and machine learning use cases.

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