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Connecting Amazon Redshift and RStudio on Amazon SageMaker

AWS Machine Learning

You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. Users can also interact with data with ODBC, JDBC, or the Amazon Redshift Data API. Solution overview.

APIs 103
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Build and train ML models using a data mesh architecture on AWS: Part 2

AWS Machine Learning

This is the second part of a series that showcases the machine learning (ML) lifecycle with a data mesh design pattern for a large enterprise with multiple lines of business (LOBs) and a Center of Excellence (CoE) for analytics and ML. In this post, we address the analytics and ML platform team as a consumer in the data mesh.

Scripts 71
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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

For example, the analytics team may curate features like customer profile, transaction history, and product catalogs in a central management account. In the context of banking, they might deduce statistical insights from account balances, identifying trends and flow patterns. The hurdle they often face is redundancy.

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A Comprehensive Guide to Virtual Call Center and Contact Centers

Hodusoft

In today’s time, starting a traditional call center will either require breaking the bank and withdrawing the entire life’s savings for the purpose or taking a huge loan and remaining indebted for a long time to come. Consider APIs and third-party integrations available to extend functionality as needed.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

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Optimal pricing for maximum profit using Amazon SageMaker

AWS Machine Learning

The results data from these jobs are stored in the Amazon S3 analytics layer. The Amazon S3 analytics layer is used to store the data that is used by the ML models for training purposes. The prepared training dataset is pushed to the analytics S3 bucket to be used by SageMaker. Train the model. In the training_script.py

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Amazon Comprehend Targeted Sentiment adds synchronous support

AWS Machine Learning

Today, we’re excited to announce the new synchronous API for targeted sentiment in Amazon Comprehend, which provides a granular understanding of the sentiments associated with specific entities in input documents. The Targeted Sentiment API provides the sentiment towards each entity.

APIs 66