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Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas

AWS Machine Learning Blog

Prerequisites Before you begin, make sure you have the following prerequisites in place: An AWS account and role with the AWS Identity and Access Management (IAM) privileges to deploy the following resources: IAM roles. A provisioned or serverless Amazon Redshift data warehouse. Choose Create stack.

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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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Use Amazon DocumentDB to build no-code machine learning solutions in Amazon SageMaker Canvas

AWS Machine Learning Blog

In this post, we discuss how to bring data stored in Amazon DocumentDB into SageMaker Canvas and use that data to build ML models for predictive analytics. Without creating and maintaining data pipelines, you will be able to power ML models with your unstructured data stored in Amazon DocumentDB.

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How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning Blog

It also includes support for new hardware like ARM (both in servers like AWS Graviton and laptops with Apple M1 ) and AWS Inferentia. Thirdly, there are improvements to demos and the extension for Spark. Follow our GitHub repo , demo repository , Slack channel , and Twitter for more documentation and examples of the DJL!

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

For this architecture, we propose an implementation on GitHub , with loosely coupled components where the backend (5), data pipelines (1, 2, 3) and front end (4) can evolve separately. Deploy the solution To install this solution in your AWS account, complete the following steps: Clone the repository on GitHub.

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How to Setup a Project in Snowpark Using a Python IDE

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Developers can seamlessly build data pipelines, ML models, and data applications with User-Defined Functions and Stored Procedures. conda activate snowflake-demo ). If your data pipeline requirements are quite straightforward—i.e., What Are Snowpark’s Differentiators? Activate the conda environment.

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How does Tableau power Salesforce Genie Customer Data Cloud?

Tableau

Every company today is being asked to do more with less, and leaders need access to fresh, trusted KPIs and data-driven insights to manage their businesses, keep ahead of the competition, and provide unparalleled customer experiences. . But good data—and actionable insights—are hard to get. Bring your own AI with AWS.

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