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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 is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. It provides a single web-based visual interface where you can perform all ML development steps, including preparing data and building, training, and deploying models.

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Configure cross-account access of Amazon Redshift clusters in Amazon SageMaker Studio using VPC peering

AWS Machine Learning Blog

Amazon Redshift is a fully managed, fast, secure, and scalable cloud data warehouse. Organizations often want to use SageMaker Studio to get predictions from data stored in a data warehouse such as Amazon Redshift. All SageMaker Studio traffic is through the specified VPC and subnets.

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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.

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Process Mining – Ist Celonis wirklich so gut? Ein Praxisbericht.

Data Science Blog

Process Mining Tools, die als pure Process Mining Software gestartet sind Hierzu gehört Celonis, das drei-köpfige und sehr geschäftstüchtige Gründer-Team, das ich im Jahr 2012 persönlich kennenlernen durfte. in Databricks oder den KI-Tools von Google, AWS und Mircosoft Azure (Azure Cognitive Services, Azure Machine Learning etc.).

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How to use Netezza Performance Server query data in Amazon Simple Storage Service (S3)

IBM Journey to AI blog

This allows data that exists in cloud object storage to be easily combined with existing data warehouse data without data movement. The advantage to NPS clients is that they can store infrequently used data in a cost-effective manner without having to move that data into a physical data warehouse table.

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Import data from Google Cloud Platform BigQuery for no-code machine learning with Amazon SageMaker Canvas

AWS Machine Learning Blog

The workflow includes the following steps: Within the SageMaker Canvas interface, the user composes a SQL query to run against the GCP BigQuery data warehouse. Athena uses the Athena Google BigQuery connector , which uses a pre-built AWS Lambda function to enable Athena federated query capabilities.

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Super charge your LLMs with RAG at scale using AWS Glue for Apache Spark

AWS Machine Learning Blog

Diverse data amplifies the need for customizable cleaning and transformation logic to handle the quirks of different sources. In this post, we will explore building a reusable RAG data pipeline on LangChain —an open source framework for building applications based on LLMs—and integrating it with AWS Glue and Amazon OpenSearch Serverless.

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