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Talk to your slide deck using multimodal foundation models on Amazon Bedrock – Part 3

AWS Machine Learning Blog

We performed a k-nearest neighbor (k-NN) search to retrieve the most relevant embedding matching the question. Archana is an aspiring member of the AI/ML technical field community at AWS. She focuses on providing technical guidance in a variety of technical domains, including AI/ML. 13636-13645.

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Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 2

AWS Machine Learning Blog

In this series, we use the slide deck Train and deploy Stable Diffusion using AWS Trainium & AWS Inferentia from the AWS Summit in Toronto, June 2023 to demonstrate the solution. We perform a k-nearest neighbor (k-NN) search to retrieve the most relevant embeddings matching the user query.

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Build a crop segmentation machine learning model with Planet data and Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

In late 2023, Planet announced a partnership with AWS to make its geospatial data available through Amazon SageMaker. In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Planet’s data is therefore a valuable resource for geospatial ML.

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Everything you should know about AI models

Dataconomy

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

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Everything you should know about AI models

Dataconomy

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

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Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 1

AWS Machine Learning Blog

In this post, we use the slide deck titled Train and deploy Stable Diffusion using AWS Trainium & AWS Inferentia from the AWS Summit in Toronto, June 2023, to demonstrate the solution. We perform a k-nearest neighbor (k=1) search to retrieve the most relevant embedding matching the user query. get('hits')[0].get('_source').get('image_path')

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Use DeepSeek with Amazon OpenSearch Service vector database and Amazon SageMaker

Flipboard

For more information, see Creating connectors for third-party ML platforms. Create an OpenSearch model When you work with machine learning (ML) models, in OpenSearch, you use OpenSearchs ml-commons plugin to create a model. You created an OpenSearch ML model group and model that you can use to create ingest and search pipelines.