ML 3478 Architecture Diagram Large

Recommend and dynamically filter items based on user context in Amazon Personalize

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Organizations are continuously investing time and effort in developing intelligent recommendation solutions to serve customized and relevant content to their users. The goals can be many: transform the user experience, generate meaningful interaction, and drive content consumption. Some of these solutions use common machine learning (ML) models built on historical interaction patterns, user demographic attributes, …

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Of barks and bots: Woof revolutionizes pet care with AI chatbot powered by Google Cloud

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As pet owners, we all want to make sure our pets are happy and healthy. But with so many different products and services competing for our attention, it can be overwhelming to decide which ones are best. That’s whereWoof comes in. We are on a mission to revolutionize the pet industry by providing the best …

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5IDER: Unified Query Rewriting for Steering, Intent Carryover, Disfluencies, Entity Carryover and Repair

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*=Equal Contributors Providing voice assistants the ability to navigate multi-turn conversations is a challenging problem. Handling multi-turn interactions requires the system to understand various conversational use-cases, such as steering, intent carryover, disfluencies, entity carryover, and repair. The complexity of this problem is compounded by the fact that these use-cases mix with each other, often appearing …

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Capture public health insights more quickly with no-code machine learning using Amazon SageMaker Canvas

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Public health organizations have a wealth of data about different types of diseases, health trends, and risk factors. Their staff has long used statistical models and regression analyses to make important decisions such as targeting populations with the highest risk factors for a disease with therapeutics, or forecasting the progression of concerning outbreaks. When public …

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