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

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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Build production-ready generative AI applications for enterprise search using Haystack pipelines and Amazon SageMaker JumpStart with LLMs

AWS Machine Learning

Enterprise search is a critical component of organizational efficiency through document digitization and knowledge management. Enterprise search covers storing documents such as digital files, indexing the documents for search, and providing relevant results based on user queries.

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Use Snowflake as a data source to train ML models with Amazon SageMaker

AWS Machine Learning

After the data is downloaded into the training instance, the custom training script performs data preparation tasks and then trains the ML model using the XGBoost Estimator. Store your Snowflake account credentials in AWS Secrets Manager. Ingest the data in a table in your Snowflake account. amazonaws.com/sagemaker-xgboost:1.5-1

Scripts 110
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Build a custom UI for Amazon Q Business

AWS Machine Learning

Amazon Q can help you get fast, relevant answers to pressing questions, solve problems, generate content, and take actions using the data and expertise found in your company’s information repositories and enterprise systems. Prerequisites For this walkthrough, you should have the following prerequisites: An AWS account set up.

APIs 98
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Train and deploy ML models in a multicloud environment using Amazon SageMaker

AWS Machine Learning

Prerequisites You should have the following prerequisites: An AWS account. As part of the setup, we define the following: A session object that provides convenience methods within the context of SageMaker and our own account. Our training script uses this location to download and prepare the training data, and then train the model.

Scripts 104
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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 75
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21 Business Analysts & Call Center Leaders Reveal the Optimal Role of the Business Analyst in Call Center Operations

Callminer

For instance, to improve key call center metrics such as first call resolution , business analysts may recommend implementing speech analytics solutions to improve agent performance management. Successful call centers use analytics to help aid, streamline and maximize customer service and sales needs…”. AmraBeganovich. Kirk Chewning.