Expedite your Genesys Cloud Amazon Lex bot design with the Amazon Lex automated chatbot designer

Favorite The rise of artificial intelligence (AI) has created opportunities to improve the customer experience in the contact center space. Machine learning (ML) technologies continually improve and power the contact center customer experience by providing solutions for capabilities like self-service bots, live call analytics, and post-call analytics. Self-service bots integrated

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Shared by AWS Machine Learning March 1, 2024

Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation

Favorite AI’s growing influence in large organizations brings crucial challenges in managing AI platforms. These include developing a scalable and operationally efficient platform that adheres to organizational compliance and security standards. Amazon SageMaker Studio offers a comprehensive set of capabilities for machine learning (ML) practitioners and data scientists. These include

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Shared by AWS Machine Learning February 29, 2024

Accelerating large-scale neural network training on CPUs with ThirdAI and AWS Graviton

Favorite This guest post is written by Vihan Lakshman, Tharun Medini, and Anshumali Shrivastava from ThirdAI. Large-scale deep learning has recently produced revolutionary advances in a vast array of fields. Although this stunning progress in artificial intelligence remains remarkable, the financial costs and energy consumption required to train these models

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Shared by AWS Machine Learning February 29, 2024

Automate Amazon SageMaker Pipelines DAG creation

Favorite Creating scalable and efficient machine learning (ML) pipelines is crucial for streamlining the development, deployment, and management of ML models. In this post, we present a framework for automating the creation of a directed acyclic graph (DAG) for Amazon SageMaker Pipelines based on simple configuration files. The framework code

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Shared by AWS Machine Learning February 29, 2024