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Amazon Bedrock Custom Model Import now generally available

AWS Machine Learning

This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API. Having a unified developer experience when accessing custom models or base models through Amazon Bedrock’s API. Ease of deployment through a fully managed, serverless, service. 2, 3, 3.1,

APIs 139
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Enhance call center efficiency using batch inference for transcript summarization with Amazon Bedrock

AWS Machine Learning

We also explore best practices for optimizing your batch inference workflows on Amazon Bedrock, helping you maximize the value of your data across different use cases and industries. Solution overview The batch inference feature in Amazon Bedrock provides a scalable solution for processing large volumes of data across various domains.

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Automate cloud security vulnerability assessment and alerting using Amazon Bedrock

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

APIs 78
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Build a secure enterprise application with Generative AI and RAG using Amazon SageMaker JumpStart

AWS Machine Learning

These SageMaker endpoints are consumed in the Amplify React application through Amazon API Gateway and AWS Lambda functions. To protect the application and APIs from inadvertent access, Amazon Cognito is integrated into Amplify React, API Gateway, and Lambda functions. For this example, we use train.cc_casebooks.jsonl.xz

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Build an agronomic data platform with Amazon SageMaker geospatial capabilities

AWS Machine Learning

This post also provides an example end-to-end notebook and GitHub repository that demonstrates SageMaker geospatial capabilities, including ML-based farm field segmentation and pre-trained geospatial models for agriculture. These differences in satellite images and frequencies also lead to differences in API capabilities and features.

APIs 87
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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Wipro has used the input filter and join functionality of SageMaker batch transformation API. The response is returned to Lambda and sent back to the application through API Gateway. The drift notification emails will look similar to the examples in Figure 8. It helped enrich the scoring data for better decision making.

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Large-scale revenue forecasting at Bosch with Amazon Forecast and Amazon SageMaker custom models

AWS Machine Learning

Bosch is a multinational corporation with entities operating in multiple sectors, including automotive, industrial solutions, and consumer goods. The neural forecasters can be bundled as a single ensemble model, or incorporated individually into Bosch’s model universe, and accessed easily via REST API endpoints.

APIs 94