AWS Unveils Machine Learning (ML) Tools for Data Science in the Cloud

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Artificial intelligence (AI) and machine learning (ML) workloads can run in any number of locations, including on-premises, edge, embedded in devices, and in the cloud .

Amazon Web Services (AWS) hopes more often organizations will choose the cloud, where it offers a growing range of services. At the AWS re:invent 2022 event in Las Vegas today, the company detailed elements of its AI/ML strategy and announced a dizzying array of feature updates and new services to help organizations better use the cloud for data science.

The cornerstone of the AWS AI/ML portfolio is the SageMaker suite of services. In a keynote at AWS re:invent Swami Sivasubramanian, vice president of database, analytics, and ML at AWS, said SageMaker enables organizations to build, train, and deploy ML models for virtually any use cases and has tools for every stage of ML development.

"Tens of thousands of customers use SageMaker ML models to make more than a trillion predictions per month," said Sivasubramanian. "Our customers are solving complex problems with SageMaker using this data to create ML models ranging from optimizing driving routes for ride-sharing applications to accelerating drug discovery."

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Register now Geospatial ML comes to SageMaker

One of the areas where SageMaker's feature set is being improved is enhanced geospatial ML capabilities.

Sivasubramanian said geospatial data can be used for a wide variety of use cases. For example, it can be used to help optimize the yield of an agricultural crop, assist in planning sustainable urban development, and can be used to identify a new location or region for opening a business. .

"Accessing high-quality geospatial data to train ML models requires working with multiple data sources and multiple vendors," he said. "These datasets are typically large and unstructured, requiring tedious data preparation before you can even start writing a single line of code to build your ML models."

With new geospatial support from SageMaker, AWS aims to make building and deploying models easier for organizations. Sivasubramanian said the new support will allow users to access geospatial data in SageMaker from different data sources with just a few clicks.

Geospatial data preparation tools are now integrated into SageMaker to help users process and enrich large datasets. SageMaker now also benefits from built-in visualization tools, allowing users to analyze data and explore model predictions on an interactive map using accelerated 3D graphics.

Sivasubramanian added that SageMaker now also provides built-in pre-trained neural networks to speed up model building for many common geospatial use cases.

ML governance gets a boost

As organizations increasingly integrate ML into different processes, there is a growing need for collaboration across groups.

Creating the permissions and governance rules that enable model sharing is another area where AWS is looking to help its users with new features in the Amazon SageMaker ML Governance service. New services include SageMaker Role Manager, Model Cards and Model Dashboard.

Sivasubramanian said SageMaker Role Manager helps organizations set critical user permissions with automated rule creation tools. The Model Cards service consists of creating a...