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An integrated experience for all your data and AI with Amazon SageMaker Unified Studio (preview)

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Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Data engineers use data warehouses, data lakes, and analytics tools to load, transform, clean, and aggregate data. Choose Create VPC.

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Tackling AI’s data challenges with IBM databases on AWS

IBM Journey to AI blog

The solution: IBM databases on AWS To solve for these challenges, IBM’s portfolio of SaaS database solutions on Amazon Web Services (AWS), enables enterprises to scale applications, analytics and AI across the hybrid cloud landscape. Let’s delve into the database portfolio from IBM available on AWS. 

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8 Data Lake Vendors to Make Your Data Life Easier in 2023

ODSC - Open Data Science

To make your data management processes easier, here’s a primer on data lakes, and our picks for a few data lake vendors worth considering. What is a data lake? First, a data lake is a centralized repository that allows users or an organization to store and analyze large volumes of data.

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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. If you’re familiar with SageMaker and writing Spark code, option B could be your choice.

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Anomaly detection in streaming time series data with online learning using Amazon Managed Service for Apache Flink

AWS Machine Learning Blog

In this post, we demonstrate how to build a robust real-time anomaly detection solution for streaming time series data using Amazon Managed Service for Apache Flink and other AWS managed services. It offers an AWS CloudFormation template for straightforward deployment in an AWS account. anomalyScore":0.0,"detectionPeriodStartTime":"2024-08-29

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Build generative AI–powered Salesforce applications with Amazon Bedrock

AWS Machine Learning Blog

In Part 3 , we demonstrate how business analysts and citizen data scientists can create machine learning (ML) models, without code, in Amazon SageMaker Canvas and deploy trained models for integration with Salesforce Einstein Studio to create powerful business applications. For this post, we use the Anthropic Claude 3 Sonnet model.

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How does Tableau power Salesforce Genie Customer Data Cloud?

Tableau

Built-in connectors bring in data from every single channel. That includes live data streams, streaming data from web and mobile, and APIs integrated with MuleSoft to bring in external data from legacy systems or proprietary data lakes. . Bring your own AI with AWS. Optimize recruiting pipelines.

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