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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.

APIs 118
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Build a custom UI for Amazon Q Business

AWS Machine Learning

This solution uses an Amazon Cognito user pool as an OAuth-compatible identity provider (IdP), which is required in order to exchange a token with AWS IAM Identity Center and later on interact with the Amazon Q Business APIs. Amazon Q uses the chat_sync API to carry out the conversation. You can also find the script on the GitHub repo.

APIs 99
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­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. Customers can also access offline store data using a Spark runtime and perform big data processing for ML feature analysis and feature engineering use cases. Table formats provide a way to abstract data files as a table.

Scripts 77
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Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

Leidos is a FORTUNE 500 science and technology solutions leader working to address some of the world’s toughest challenges in the defense, intelligence, homeland security, civil, and healthcare markets. Applications and services can call the deployed endpoint directly or through a deployed serverless Amazon API Gateway architecture.

Scripts 100
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Reduce cost and development time with Amazon SageMaker Pipelines local mode

AWS Machine Learning

Developers usually test their processing and training scripts locally, but the pipelines themselves are typically tested in the cloud. One of the main drivers for new innovations and applications in ML is the availability and amount of data along with cheaper compute options. Build your pipeline.

Scripts 76
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Build and train computer vision models to detect car positions in images using Amazon SageMaker and Amazon Rekognition

AWS Machine Learning

Finally, we show how you can integrate this car pose detection solution into your existing web application using services like Amazon API Gateway and AWS Amplify. For each option, we host an AWS Lambda function behind an API Gateway that is exposed to our mock application. iterdir(): if p_file.suffix == ".pth":

APIs 66
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How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

AWS Machine Learning

When the message is received by the SQS queue, it triggers the AWS Lambda function to make an API call to the Amp catalog service. The Lambda function retrieves the desired show metadata, filters the metadata, and then sends the output metadata to Amazon Kinesis Data Streams. Data Engineer for Amp on Amazon. About the authors.