Remove ui-test-automation
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Moderate your Amazon IVS live stream using Amazon Rekognition

AWS Machine Learning

An automated moderation solution supporting a human in the loop (HITL) is increasingly needed. Amazon Rekognition Content Moderation , a capability of Amazon Rekognition , automates and streamlines image and video moderation workflows without requiring machine learning (ML) experience. For example: * applies to all channels.

APIs 88
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Voicebot vs. Chatbot – what’s the difference?

Babelforce

They’re dominating the headlines, they’re gaining new capabilities, and sometimes they even pass the Turing test. A Question of Conversational User Interfaces Do Voicebots and Chatbots have the same automation capability? In both cases, the conversational UI allows a user to interact in a conversational way with a computer system.

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Improve LLM performance with human and AI feedback on Amazon SageMaker for Amazon Engineering

AWS Machine Learning

To achieve this, we developed a feedback collection module in the UI, as shown in the following figure, and stored the web session information and user feedback in Amazon DynamoDB. We tested the methodology using the Amazon D&C documents with a Mistral-7B model on SageMaker JumpStart.

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Deploy an MLOps solution that hosts your model endpoints in AWS Lambda

AWS Machine Learning

Automating model training and retraining, having a model registry, and tracking experiments and deployment are some of the key challenges. The main pipeline, Model building (Pipeline) , is another CodeCommit repository that automates running your SageMaker pipelines. tests — Contains unit and integration tests.

Scripts 75
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Build a foundation model (FM) powered customer service bot with agents for Amazon Bedrock

AWS Machine Learning

Components in agents for Amazon Bedrock Behind the scenes, agents for Amazon Bedrock automate the prompt engineering and orchestration of user-requested tasks. Test and deploy agents for Amazon Bedrock Test the agent : After the agent is created, a dialog box shows the agent overview along with a working draft.

APIs 86
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Deploy self-service question answering with the QnABot on AWS solution powered by Amazon Lex with Amazon Kendra and large language models

AWS Machine Learning

See Semantic question matching, using Large Language Model Text Embeddings for more details on how to test and tune the threshold settings. Experiment (using the TEST tab in the designer) to find the best values to use for the embedding threshold settings to get the behavior you want. We discuss two such use cases in this section.

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The bird’s eye: watchRTC makes monitoring, analyzing, and vizualizing your webRTC data a breeze

Spearline

At Spearline, we’re committed to transforming the future of webRTC testing, monitoring, and analytics. O ur suite of webRTC testing, monitoring, and analytics solutions provide a raft of real-time and aggregate global and localized testing metrics. It is then making this data available to your IT team.

Metrics 95