Remove Auto-complete Remove Automation Remove Metadata
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Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

AWS Machine Learning Blog

Enterprises may want to add custom metadata like document types (W-2 forms or paystubs), various entity types such as names, organization, and address, in addition to the standard metadata like file type, date created, or size to extend the intelligent search while ingesting the documents.

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Scale AI training and inference for drug discovery through Amazon EKS and Karpenter

AWS Machine Learning Blog

The platform both enables our AI—by supplying data to refine our models—and is enabled by it, capitalizing on opportunities for automated decision-making and data processing. We use Amazon EKS and were looking for the best solution to auto scale our worker nodes. This enables all steps to be completed from a web browser.

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Build RAG-based generative AI applications in AWS using Amazon FSx for NetApp ONTAP with Amazon Bedrock

AWS Machine Learning Blog

Our solution uses an FSx for ONTAP file system as the source of unstructured data and continuously populates an Amazon OpenSearch Serverless vector database with the user’s existing files and folders and associated metadata. Prerequisites Complete the following prerequisite steps: Make sure you have model access in Amazon Bedrock.

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How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

This requires not only well-designed features and ML architecture, but also data preparation and ML pipelines that can automate the retraining process. To solve this problem, we make the ML solution auto-deployable with a few configuration changes. ML engineers no longer need to manage this training metadata separately.

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From Solo Notebooks to Collaborative Powerhouse: VS Code Extensions for Data Science and ML Teams

Towards AI

Auto-Completion and Refactoring: Enhances coding efficiency and readability. The Python Indent extension automates indentation management, ensuring that your code adheres to best practices. Key Features: Comprehensive Versioning: Beyond just data, DVC versions metadata, plots, models, and entire ML pipelines.

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How Veritone uses Amazon Bedrock, Amazon Rekognition, Amazon Transcribe, and information retrieval to update their video search pipeline

AWS Machine Learning Blog

With a decade of enterprise AI experience, Veritone supports the public sector, working with US federal government agencies, state and local government, law enforcement agencies, and legal organizations to automate and simplify evidence management, redaction, person-of-interest tracking, and eDiscovery.

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Accelerate pre-training of Mistral’s Mathstral model with highly resilient clusters on Amazon SageMaker HyperPod

AWS Machine Learning Blog

With the SageMaker HyperPod auto-resume functionality, the service can dynamically swap out unhealthy nodes for spare ones to ensure the seamless continuation of the workload. Also included are SageMaker HyperPod cluster software packages, which support features such as cluster health check and auto-resume.