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Govern generative AI in the enterprise with Amazon SageMaker Canvas

AWS Machine Learning Blog

This simplifies access to generative artificial intelligence (AI) capabilities to business analysts and data scientists without the need for technical knowledge or having to write code, thereby accelerating productivity. Provide the AWS Region, account, and model IDs appropriate for your environment.

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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning Blog

With the introduction of EMR Serverless support for Apache Livy endpoints , SageMaker Studio users can now seamlessly integrate their Jupyter notebooks running sparkmagic kernels with the powerful data processing capabilities of EMR Serverless. This same interface is also used for provisioning EMR clusters.

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Announcing Amazon S3 access point support for Amazon SageMaker Data Wrangler

AWS Machine Learning Blog

We’re excited to announce Amazon SageMaker Data Wrangler support for Amazon S3 Access Points. In this post, we walk you through importing data from, and exporting data to, an S3 access point in SageMaker Data Wrangler. Configure your AWS Identity and Access Management (IAM) role with the necessary policies.

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Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning Blog

IAM role – SageMaker requires an AWS Identity and Access Management (IAM) role to be assigned to a SageMaker Studio domain or user profile to manage permissions effectively. An execution role update may be required to bring in data browsing and the SQL run feature. You need to create AWS Glue connections with specific connection types.

SQL 126
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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

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Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. To do this, we provide an AWS CloudFormation template to create a stack that contains the resources.

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Machine learning with decentralized training data using federated learning on Amazon SageMaker

AWS Machine Learning Blog

With SageMaker, data scientists and developers can quickly build and train ML models, and then deploy them into a production-ready hosted environment. In this post, we demonstrate how to use the managed ML platform to provide a notebook experience environment and perform federated learning across AWS accounts, using SageMaker training jobs.

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU. Around this time, industry observers reported NVIDIA’s strategy pivoting from its traditional gaming and graphics focus to moving into scientific computing and data analytics. Work by Hinton et al.

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