Remove insights ai-data data-annotation
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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning

One of the key drivers of Philips’ innovation strategy is artificial intelligence (AI), which enables the creation of smart and personalized products and services that can improve health outcomes, enhance customer experience, and optimize operational efficiency.

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Automate PDF pre-labeling for Amazon Comprehend

AWS Machine Learning

Amazon Comprehend is a natural-language processing (NLP) service that provides pre-trained and custom APIs to derive insights from textual data. To train a custom model, you first prepare training data by manually annotating entities in documents. The full code is available on the GitHub repo.

Banking 93
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Announcing Rekogniton Custom Moderation: Enhance accuracy of pre-trained Rekognition moderation models with your data

AWS Machine Learning

Content moderation in Amazon Rekognition Amazon Rekognition is a managed artificial intelligence (AI) service that offers pre-trained and customizable computer vision capabilities to extract information and insights from images and videos. You can train a custom adapter with as few as 20 annotated images in less than 1 hour.

APIs 110
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Amazon SageMaker JumpStart now offers Amazon Comprehend notebooks for custom classification and custom entity detection

AWS Machine Learning

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to discover insights from text. Amazon Comprehend helps reduce the complexity by providing automatic annotation and model development to create a custom entity recognition model. Feel free to customize the generated code per your needs.

APIs 76
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Five reasons why AI teams should in-source data labeling

Interactions

Annotating data is hard work. Depending on the speech and natural language technology you’re developing, few innovative projects can be done with the same type of data — or even the same annotation scheme. This kind of data is hard to find, so we needed to invest in data annotation.

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3 Costly Points of Failure in Omnichannel Customer Experience and How to Fix Them

TechSee

No Vision, No AI, No Service. Use visual data to enhance self-service with context & customization. Sessions are impersonal, instructions and guidance provided are generic, and the visual data accumulated is not being used to its full potential. Visual data can also influence escalation next steps.

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3 Reasons Customers Say They Don’t Trust Chatbots, and What You Can Do About It

TechSee

Add computer vision AI to chatbots to reduce friction points in issue identification. Computer vision AI to identify the issue. Though chatbot AI technology has greatly matured in the last few years, it still struggles to diagnose issues and or show customers how to solve them. 2: Chatbots Don’t Solve the Problem.

Chatbots 109