Run ML inference on unplanned and spiky traffic using Amazon SageMaker multi-model endpoints

Favorite Amazon SageMaker multi-model endpoints (MMEs) are a fully managed capability of SageMaker inference that allows you to deploy thousands of models on a single endpoint. Previously, MMEs pre-determinedly allocated CPU computing power to models statically regardless the model traffic load, using Multi Model Server (MMS) as its model server.

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Shared by AWS Machine Learning February 19, 2024

Build generative AI chatbots using prompt engineering with Amazon Redshift and Amazon Bedrock

Favorite With the advent of generative AI solutions, organizations are finding different ways to apply these technologies to gain edge over their competitors. Intelligent applications, powered by advanced foundation models (FMs) trained on huge datasets, can now understand natural language, interpret meaning and intent, and generate contextually relevant and human-like

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Shared by AWS Machine Learning February 14, 2024

Enhance Amazon Connect and Lex with generative AI capabilities

Favorite Effective self-service options are becoming increasingly critical for contact centers, but implementing them well presents unique challenges. Amazon Lex provides your Amazon Connect contact center with chatbot functionalities such as automatic speech recognition (ASR) and natural language understanding (NLU) capabilities through voice and text channels. The bot takes natural

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Shared by AWS Machine Learning February 14, 2024