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How Dynamic Scripting Can Improve Your Agents’ Performance

Calltools

This is where dynamic scripting comes in. It customizes call scripts in real time, ensuring every single conversation is more relevant and personal. Dynamic scripting lets you cater scripts for different customers, demographics, and campaigns. What Is Dynamic Scripting? Dynamic scripting can help with all this.

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2020 Call Center Metrics: 6 Key Metrics for Your Call Center Dashboard

Callminer

At the heart of most technological optimizations implemented within a successful call center are fine-tuned metrics. Keeping tabs on the right metrics can make consistent improvement notably simpler over the long term. However, not all metrics make sense for a growing call center to monitor. Peak Hour Traffic.

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Accelerate pre-training of Mistral’s Mathstral model with highly resilient clusters on Amazon SageMaker HyperPod

AWS Machine Learning

It is important to consider the massive amount of compute often required to train these models. When using compute clusters of massive size, a single failure can often throw a training job off course and may require multiple hours of discovery and remediation from customers. In recent years, FM sizes have been increasing.

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Use your own training scripts and automatically select the best model using hyperparameter optimization in Amazon SageMaker

AWS Machine Learning

The success of any machine learning (ML) pipeline depends not just on the quality of model used, but also the ability to train and iterate upon this model. However, doing this tuning manually can often be cumbersome due to the size of the search space, sometimes involving thousands of training iterations. Solution overview.

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Scalable training platform with Amazon SageMaker HyperPod for innovation: a video generation case study

AWS Machine Learning

Trained on the Amazon SageMaker HyperPod , Dream Machine excels in creating consistent characters, smooth motion, and dynamic camera movements. Model parallel training becomes necessary when the total model footprint (model weights, gradients, and optimizer states) exceeds the memory of a single GPU.

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Pre-training genomic language models using AWS HealthOmics and Amazon SageMaker

AWS Machine Learning

In this blog post and open source project , we show you how you can pre-train a genomics language model, HyenaDNA , using your genomic data in the AWS Cloud. Amazon SageMaker Amazon SageMaker is a fully managed ML service offered by AWS, designed to reduce the time and cost associated with training and tuning ML models at scale.

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Train, optimize, and deploy models on edge devices using Amazon SageMaker and Qualcomm AI Hub

AWS Machine Learning

SageMaker is an excellent choice for model training, because it reduces the time and cost to train and tune ML models at scale without the need to manage infrastructure. Because you pay only for what you use, you can manage your training costs more effectively. The final model artifact is saved to an S3 bucket.

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