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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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. Automated pipelining and workflow orchestration: Platforms should provide tools for automated pipelining and workflow orchestration, enabling you to define and manage complex ML pipelines.

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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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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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Evaluate large language models for your machine translation tasks on AWS

AWS Machine Learning Blog

Localization relies on both automation and humans-in-the-loop in a process called Machine Translation Post Editing (MTPE). When using the FAISS adapter, translation units are stored into a local FAISS index along with the metadata. One of LLMs most fascinating strengths is their inherent ability to understand context.

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Streamline diarization using AI as an assistive technology: ZOO Digital’s story

AWS Machine Learning Blog

This time-consuming process must be completed before content can be dubbed into another language. Through automation, ZOO Digital aims to achieve localization in under 30 minutes. However, the supply of skilled people is being outstripped by the increasing demand for content, requiring automation to assist with localization workflows.

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