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Designing generative AI workloads for resilience

AWS Machine Learning Blog

Data pipelines In cases where you need to provide contextual data to the foundation model using the RAG pattern, you need a data pipeline that can ingest the source data, convert it to embedding vectors, and store the embedding vectors in a vector database.

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How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

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Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. He currently is working on Generative AI for data integration. Clay Elmore is an AI/ML Specialist Solutions Architect at AWS. He is the author of the upcoming book “What’s Your Problem?”

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Train An Emotion Recognition Model Using Multiple Datasets-Part 1

Mlearning.ai

FER, Facial Expression Recognition, is an open-source dataset released in 2013. BECOME a WRITER at MLearning.ai // FREE ML Tools // Clearview AI Mlearning.ai Let’s take a moment to break down the project architecture shown above before we dive into the code. What is the FER dataset? If you have any questions, feel free to reach out.

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Architect a mature generative AI foundation on AWS

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This means gateway, data pipelines, data storage, training infrastructure, and other components are deployed on an isolated infrastructure per tenant. Since 2013 he has helped AWS customers adopt AI/ML technology as a Solutions Architect.

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