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25 Call Center Leaders Share the Most Effective Ways to Boost Contact Center Efficiency

Callminer

Have the best technology – Finicky technology that makes day-to-day functions difficult, adds to the frustration and takes up far more time and man-power than most realize. HEG uses proprietary technology, like EduMaximizer, to deliver targeted enrollment leads to for-profit and nonprofit education clients. Too many portals…”.

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18 Contact Center Strategies That Actually Work

JustCall

Look at the product from the customers’ point of view to better model your contact center scripts. These technologies are proactive and autonomous to a certain extent, helping you expedite your operations backed with data-heavy insights. You can further categorize the scripts to help with a more extensive training session.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

ML operationalization summary As defined in the post MLOps foundation roadmap for enterprises with Amazon SageMaker , ML and operations (MLOps) is the combination of people, processes, and technology to productionize machine learning (ML) solutions efficiently. One of these new notions is the foundation model (FM).

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Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2

AWS Machine Learning

For benchmark analysis, we considered the task of predicting the in-hospital mortality of patients [2]. You can place the data in any folder of your choice, as long as the path is consistently referenced in the training script and has access enabled. Import the data loader into the training script. and data_loader.py

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How Games24x7 transformed their retraining MLOps pipelines with Amazon SageMaker

AWS Machine Learning

Games24x7 is one of India’s most valuable multi-game platforms and entertains over 100 million gamers across various skill games. The code to invoke the pipeline script is available in the Studio notebooks, and we can change the hyperparameters and input/output when invoking the pipeline.

Scripts 81
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Fine-tune Llama 2 for text generation on Amazon SageMaker JumpStart

AWS Machine Learning

nn For performance benchmarking of different models on the Dolly and Dialogsum dataset, refer to the Performance benchmarking section in the appendix at the end of this post. He holds PhD and MS degrees in Electrical Engineering from the University of Texas at Austin and an MS in Computer Science from Georgia Institute of Technology.