Roundup of re:Invent 2021 Amazon SageMaker announcements

At re: Invent 2021, AWS revealed a number of new Amazon SageMaker features that make artificial intelligence (ML) available to new kinds of users while continuing to increase performance and minimize cost for data scientists and ML experts. In this post, we provide a summary of these statements, along with resources for you to get more details on every one.
ML for all
As ML adoption grows, ML skills are in higher need. AWS revealed that Amazon SageMaker Canvas is expanding access to ML by supplying business experts with a visual point-and-click user interface that lets them create precise ML predictions on their own– without requiring any ML experience or having to write a single line of code.
Processing disorganized and structured data at scale
As more people start utilizing ML in their day-to-day work, the need to identify datasets for training grows and information science teams cant keep up with the growing demand. SageMaker Ground Truth Plus provides an expert workforce that is trained on ML jobs and can assist satisfy your data security, personal privacy, and compliance requirements.
Enhance the performance and cost of structure, training, and deploying ML models
AWS is also continuing to make it much easier and cheaper for information researchers and developers to build and prepare information, train, and deploy ML designs.
For building ML models, AWS launched enhancements to Amazon SageMaker Studio so that you can now do information processing, analytics, and ML workflows in one combined note pad. From this universal notebook, you can access a wide variety of data sources and compose code for any transformation for a variety of information work.
In addition to making training quicker, AWS released a brand-new compiler, Amazon SageMaker Training Compiler, which can accelerate training by up to 50% through graph- and kernel-level optimizations to use GPUs more efficiently. SageMaker Training Compiler is incorporated with variations of TensorFlow and PyTorch in SageMaker. Therefore, you can speed up training in these popular frameworks with minimal code modifications.
And finally, for reasoning, AWS revealed two features to minimize inference expenses. Amazon SageMaker Serverless Inference (preview) lets you deploy ML models on pay-per-use rates without fretting about servers or clusters for usage cases with periodic traffic patterns. In addition, Amazon SageMaker Inference Recommender helps you pick the best offered compute instance and configuration to deploy ML models for optimal inference efficiency and cost.
Find out ML totally free
Amazon SageMaker Studio Lab (sneak peek) is a totally free ML notebook environment that makes it easy for anybody to experiment with building and training ML models without requiring to set up facilities or handle identity and gain access to. SageMaker Studio Lab accelerates design building through GitHub combination, and it comes preconfigured with the most popular ML frameworks, libraries, and tools to get you started immediately.
To read more about these features, go to the Amazon SageMaker website.

As ML adoption grows, ML skills are in greater need. AWS announced that Amazon SageMaker Canvas is expanding access to ML by offering company experts with a visual point-and-click user interface that lets them produce precise ML predictions on their own– without requiring any ML experience or having to write a single line of code. SageMaker Ground Truth Plus provides a professional workforce that is trained on ML jobs and can assist meet your data security, personal privacy, and compliance requirements. Amazon SageMaker Studio Lab (sneak peek) is a free ML note pad environment that makes it easy for anyone to experiment with building and training ML models without needing to set up infrastructure or manage identity and gain access to. Her objective is to make it easy for customers to develop, train, and release ML designs utilizing Amazon SageMaker.

About the Author
Kimberly Madia is the Sr. Manager of Product Marketing, AWS, heading up item marketing for AWS Machine Learning services. Her objective is to make it easy for clients to develop, train, and deploy ML designs using Amazon SageMaker. For fun beyond work, Kimberly likes to prepare, check out, and run on the San Francisco Bay Trail.

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