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Self-Service for Contact Center: The Definitive Guide

NobelBiz

Research from Benchmark Portal found that, on average, 15% of customer inquiries are handled through self-service. The post Self-Service for Contact Center: The Definitive Guide appeared first on NobelBiz. You guessed it; it’s money. Here’s the simple math. Get in touch with one of our experts here !

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Fine-tune large multimodal models using Amazon SageMaker

AWS Machine Learning

The prospect of fine-tuning open source multimodal models like LLaVA are highly appealing because of their cost effectiveness, scalability, and impressive performance on multimodal benchmarks. It sets up a SageMaker training job to run the custom training script from LLaVA. For full parameter fine-tuning, ml.p4d.24xlarge

Scripts 121
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Call Center Metrics: Examples, Tips & Best Practices

Callminer

Call on experienced managers for guidance in setting up benchmarks. “Experienced call center managers are helpful in setting up the initial performance benchmarks for a new outbound call center program. These benchmarks are, at first, estimated based on the past performance of similar outbound call center projects.

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How Games24x7 transformed their retraining MLOps pipelines with Amazon SageMaker

AWS Machine Learning

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. This is quite different from our earlier method where we had all the parameters hard coded within the scripts and all the processes were inextricably linked.

Scripts 96
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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning

This was the perfect place to start for our prototype—not only would Axfood gain a new AI/ML platform, but we would also get a chance to benchmark our ML capabilities and learn from leading AWS experts. If discrepancies arise, a business logic within the postprocessing script assesses whether retraining the model is necessary.

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Improve price performance of your model training using Amazon SageMaker heterogeneous clusters

AWS Machine Learning

Our benchmarks show up to 46% price performance benefit after enabling heterogeneous clusters in a CPU-bound TensorFlow computer vision model training. Performance benchmark results. You can build logic in your training script to assign the instance groups to certain training and data processing tasks.

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Image classification model selection using Amazon SageMaker JumpStart

AWS Machine Learning

The former question addresses model selection across model architectures, while the latter question concerns benchmarking trained models against a test dataset. This post provides details on how to implement large-scale Amazon SageMaker benchmarking and model selection tasks. swin-large-patch4-window7-224 195.4M efficientnet-b5 29.0M

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