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Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

AWS Machine Learning Blog

These models are designed to understand and generate text about images, bridging the gap between visual information and natural language. After the documents are ingested in OpenSearch Service (this is a one-time setup step), we deploy the full end-to-end multimodal chat assistant using an AWS CloudFormation template.

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How Vidmob is using generative AI to transform its creative data landscape

AWS Machine Learning Blog

In this post, we illustrate how Vidmob , a creative data company, worked with the AWS Generative AI Innovation Center (GenAIIC) team to uncover meaningful insights at scale within creative data using Amazon Bedrock. The chatbot built by AWS GenAIIC would take in this tag data and retrieve insights.

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Automating product description generation with Amazon Bedrock

AWS Machine Learning Blog

Creating engaging and informative product descriptions for a vast catalog is a monumental task, especially for global ecommerce platforms. This solution is available in the AWS Solutions Library. The README file contains all the information you need to get started, from requirements to deployment guidelines.

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Real value, real time: Production AI with Amazon SageMaker and Tecton

AWS Machine Learning Blog

In a fraud detection system, when someone makes a transaction (such as buying something online), your app might follow these steps: It checks with other services to get more information (for example, “Is this merchant known to be risky?”) You can also find Tecton at AWS re:Invent. This process is shown in the following diagram.

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Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

AWS Machine Learning Blog

In this post, we discuss how the AWS AI/ML team collaborated with the Merck Human Health IT MLOps team to build a solution that uses an automated workflow for ML model approval and promotion with human intervention in the middle. A model developer typically starts to work in an individual ML development environment within Amazon SageMaker.

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Reduce call hold time and improve customer experience with self-service virtual agents using Amazon Connect and Amazon Lex

AWS Machine Learning Blog

KYTC DVR’s challenges The KYTC DVR supports, assists and provides information related to vehicle registration, driver licenses, and commercial vehicle credentials to nearly 5 million constituents. “In The contact center is powered by Amazon Connect, and Max, the virtual agent, is powered by Amazon Lex and the AWS QnABot solution.

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Accelerate disaster response with computer vision for satellite imagery using Amazon SageMaker and Amazon Augmented AI

AWS Machine Learning Blog

AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art machine learning (ML) models, reducing barriers for these types of use cases. For more information, refer to Preview: Use Amazon SageMaker to Build, Train, and Deploy ML Models Using Geospatial Data.

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