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

AWS Machine Learning

In this post, we build a secure enterprise application using AWS Amplify that invokes an Amazon SageMaker JumpStart foundation model, Amazon SageMaker endpoints, and Amazon OpenSearch Service to explain how to create text-to-text or text-to-image and Retrieval Augmented Generation (RAG).

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Build an agronomic data platform with Amazon SageMaker geospatial capabilities

AWS Machine Learning

Key decisions include what crops to plant, how much fertilizer to apply, how to control pests, and when to harvest. These differences in satellite images and frequencies also lead to differences in API capabilities and features. In his free time, he enjoys hiking, travelling, and spending time with family and friends.

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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. Conclusion.

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Automated exploratory data analysis and model operationalization framework with a human in the loop

AWS Machine Learning

For instructions on assigning permissions to the role, refer to Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference. The Step Functions state machine, S3 bucket, Amazon API Gateway resources, and Lambda function codes are stored in the GitHub repo. The following figure illustrates our Step Function workflow.

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Configure an AWS DeepRacer environment for training and log analysis using the AWS CDK

AWS Machine Learning

In this post, we shared how you can configure a dynamic AWS DeepRacer environment and set up selective services to accelerate the journey of users on the AWS platform. He works with AABG to develop and implement innovative cloud solutions, and specializes in infrastructure as code and cloud security.

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Bring SageMaker Autopilot into your MLOps processes using a custom SageMaker Project

AWS Machine Learning

This post showcases how to have a repeatable process with low-code tools like Amazon SageMaker Autopilot such that it can be seamlessly integrated into your environment, so you don’t have to orchestrate this end-to-end workflow on your own. For instructions on how to include or remove transformations in Data Wrangler, refer to Transform Data.

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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. In this section, we’ll show you how to fine-tune the Llama 3.2

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