At re:Invent 2021, AWS announced several new Amazon SageMaker features that make machine learning (ML) accessible to new types of users while continuing to increase performance and reduce cost for data scientists and ML experts. In this post, we provide a summary of these announcements, along with resources for you to get more details on each one.

ML for all

As ML adoption grows, ML skills are in higher demand. To help meet this growing demand, AWS is expanding the reach of ML beyond data scientists and developers to the broader business user community, including line-of-business analysts supporting finance, marketing, operations, and HR teams. AWS announced that Amazon SageMaker Canvas is expanding access to ML by providing business analysts with a visual point-and-click interface that lets them generate accurate ML predictions on their own—without requiring any ML experience or having to write a single line of code. Get started on a two-month free trial including up to 10 ML models with up to 1 million cells of data free.

Processing structured and unstructured data at scale

As more people start using ML in their daily work, the need to label datasets for training grows and data science teams can’t keep up with the growing demand. AWS announced Amazon SageMaker Ground Truth Plus to make it easy to create high-quality training datasets without having to build labeling applications or manage labeling workforces on your own. SageMaker Ground Truth Plus provides an expert workforce that is trained on ML tasks and can help meet your data security, privacy, and compliance requirements. Simply upload your data, and Amazon SageMaker Ground Truth Plus creates data labeling workflows and manages workflows on your behalf. Request a pilot to get started.

Optimize the performance and cost of building, training, and deploying ML models

AWS is also continuing to make it easier and cheaper for data scientists and developers to prepare data and build, train, and deploy ML models.

First, for building ML models, AWS released enhancements to Amazon SageMaker Studio so that you can now do data processing, analytics, and ML workflows in one unified notebook. From this universal notebook, you can access a wide range of data sources and write code for any transformation for a variety of data workloads.

In addition to making training faster, AWS launched a 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 integrated with versions of TensorFlow and PyTorch in SageMaker. Therefore, you can speed up training in these popular frameworks with minimal code changes.

And lastly, for inference, AWS announced two features to reduce inference costs. Amazon SageMaker Serverless Inference (preview) lets you deploy ML models on pay-per-use pricing without worrying about servers or clusters for use cases with intermittent traffic patterns. In addition, Amazon SageMaker Inference Recommender helps you choose the best available compute instance and configuration to deploy ML models for optimal inference performance and cost.

Learn ML for free

Amazon SageMaker Studio Lab (preview) is a free ML notebook environment that makes it easy for anyone to experiment with building and training ML models without needing to configure infrastructure or manage identity and access. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab offers 15 GB of dedicated storage for your ML projects and automatically saves your work so that you don’t need to restart in between sessions. It’s as easy as closing your laptop and coming back later. All you need is a valid email ID to get started with SageMaker Studio Lab.

To learn more about these features, visit the Amazon SageMaker website.

About the Author

Kimberly Madia is the Sr. Manager of Product Marketing, AWS, heading up product marketing for AWS Machine Learning services. Her goal is to make it easy for customers to build, train, and deploy ML models using Amazon SageMaker. For fun outside of work, Kimberly likes to cook, read, and run on the San Francisco Bay Trail.

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