Remove call-monitoring-parameters
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101 Call Monitoring Parameters for Quality and Coaching

Voxjar

That’s why choosing the right call monitoring parameters to track call quality and measure rep performance needs to be a top priority. Good parameters are measurable and clearly defined (something you can test through calibration sessions with management, supervisors, and reps – post on this coming soon).

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Best Practices to Monitor Your Remote Call Center Agents

ShepHyken

Sh e shares best practices supervisors can use for monitoring call center agents in a work-from-home scenario. Now, the real question is, how do we monitor remote agents to ensure maximum efficiency with minimum supervision required. Real-time Remote Monitoring. Comprehensive Call Center Metrics Report. Here’s how!

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Artificial intelligence (AI) and machine learning (ML) offerings from Amazon Web Services (AWS) , along with integrated monitoring and notification services, help organizations achieve the required level of automation, scalability, and model quality at optimal cost. The pipeline should also be launched by the data push event to S3.

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Live Meeting Assistant with Amazon Transcribe, Amazon Bedrock, and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

You’ve probably also experienced the need to quickly fact-check something that’s been said, or look up information to answer a question that’s just been asked in the call. You are responsible for complying with legal, corporate, and ethical restrictions that apply to recording meetings and calls.

APIs 109
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Unlock personalized experiences powered by AI using Amazon Personalize and Amazon OpenSearch Service

AWS Machine Learning

OpenSearch is a scalable, flexible, and extensible open source software suite for search, analytics, security monitoring, and observability applications, licensed under the Apache 2.0 OpenSearch uses a probabilistic ranking framework called BM-25 to calculate relevance scores. You specify a weight parameter (between 0.0–1.0)

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Amazon SageMaker Automatic Model Tuning now automatically chooses tuning configurations to improve usability and cost efficiency

AWS Machine Learning

Hyperparameter overview When training any machine learning (ML) model, you are generally dealing with three types of data: input data (also called the training data), model parameters, and hyperparameters. You use the input data to train your model, which in effect learns your model parameters.

APIs 77
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Monitor embedding drift for LLMs deployed from Amazon SageMaker JumpStart

AWS Machine Learning

Overview of RAG The RAG pattern lets you retrieve knowledge from external sources, such as PDF documents, wiki articles, or call transcripts, and then use that knowledge to augment the instruction prompt sent to the LLM. This allows the LLM to reference more relevant information when generating a response. example.com for all subdomains.