Remove AWS Remove Data Preparation Remove Internet of Things
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Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 3

AWS Machine Learning Blog

We show you how to use AWS IoT Greengrass to manage model inference at the edge and how to automate the process using AWS Step Functions and other AWS services. AWS IoT Greengrass is an Internet of Things (IoT) open-source edge runtime and cloud service that helps you build, deploy, and manage edge device software.

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Automatically redact PII for machine learning using Amazon SageMaker Data Wrangler

AWS Machine Learning Blog

Additionally, using Amazon Comprehend with AWS PrivateLink means that customer data never leaves the AWS network and is continuously secured with the same data access and privacy controls as the rest of your applications. For more details, refer to Integrating SageMaker Data Wrangler with SageMaker Pipelines.

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HAYAT HOLDING uses Amazon SageMaker to increase product quality and optimize manufacturing output, saving $300,000 annually

AWS Machine Learning Blog

Input data is streamed from the plant via OPC-UA through SiteWise Edge Gateway in AWS IoT Greengrass. Model training and optimization with SageMaker automatic model tuning Prior to the model training, a set of data preparation activities are performed. Samples are sent to a laboratory for quality tests.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning Blog

The following is an example of notable proprietary FMs available in AWS (July 2023). The following is an example of notable open-source FM available in AWS (July 2023). Additions are required in historical data preparation, model evaluation, and monitoring. The following figure illustrates their journey.

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Embodied AI Chess with Amazon Bedrock

AWS Machine Learning Blog

Solution overview The chess demo uses a broad spectrum of AWS services to create an interactive and engaging gaming experience. The following architecture diagram illustrates the service integration and data flow in the demo. The application’s core backend functionality is handled by a combination of Unit and Pipeline Resolvers.

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