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Real value, real time: Production AI with Amazon SageMaker and Tecton

AWS Machine Learning Blog

Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production. Using SageMaker, you can build, train and deploy ML models.

ML 102
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Build an AI-powered document processing platform with open source NER model and LLM on Amazon SageMaker

Flipboard

Rather than maintaining constantly running endpoints, the system creates them on demand when document processing begins and automatically stops them upon completion. This endpoint based architecture provides decoupling between the other processing, allowing independent scaling, versioning, and maintenance of each component.

AWS 110
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Build a dynamic, role-based AI agent using Amazon Bedrock inline agents

AWS Machine Learning Blog

Employees and managers see different levels of company policy information, with managers getting additional access to confidential data like performance review and compensation details. The role information is also used to configure metadata filtering in the knowledge bases to generate relevant responses.

AI 105
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Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

AWS Machine Learning Blog

These models are designed to understand and generate text about images, bridging the gap between visual information and natural language. The following system architecture represents the logic flow when a user uploads an image, asks a question, and receives a text response grounded by the text dataset stored in OpenSearch.

AWS 129
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Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

AWS Machine Learning Blog

ML Engineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. Model developers often work together in developing ML models and require a robust MLOps platform to work in.

ML 133
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Accelerate disaster response with computer vision for satellite imagery using Amazon SageMaker and Amazon Augmented AI

AWS Machine Learning Blog

AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art machine learning (ML) models, reducing barriers for these types of use cases. For more information, refer to Preview: Use Amazon SageMaker to Build, Train, and Deploy ML Models Using Geospatial Data.

ML 102
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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

Investment professionals face the mounting challenge of processing vast amounts of data to make timely, informed decisions. This challenge is particularly acute in credit markets, where the complexity of information and the need for quick, accurate insights directly impacts investment outcomes.

AWS 116