article thumbnail

Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning

Using its enterprise software, FloTorch conducted an extensive comparison between Amazon Nova models and OpenAIs GPT-4o models with the Comprehensive Retrieval Augmented Generation (CRAG) benchmark dataset. FloTorch used these queries and their ground truth answers to create a subset benchmark dataset.

article thumbnail

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

Callminer

To get the most out of this metric, use it to inform budgeting and infrastructure-related decisions as opposed to using it for agent benchmarking purposes. Although some consumers place greater importance on accuracy than speed, AHT still has a special place in a call center’s daily operations. Customer Effort Score.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Live Chat: To script or not to script

RapportBoost

Chat scripts are a handy tool, especially for chat agents who find themselves often responding to related customer inquiries. Chat scripts, or canned responses, help companies ensure quality control, implement precise language for optimal results, and increase customer happiness. Not all companies implement chat scripts with success.

Scripts 80
article thumbnail

Accelerate digital pathology slide annotation workflows on AWS using H-optimus-0

AWS Machine Learning

This sets a new benchmark for state-of-the-art performance in critical medical diagnostic tasks, from identifying cancerous cells to detecting genetic abnormalities in tumors. script that automatically downloads and organizes the data in your EFS storage. The AWS CloudFormation template for this solution uses t3.medium

article thumbnail

What is Call Center Quality Assurance?

OctopusTech

In the case of a call center, you will mark the performance of the agents against key performance indicators like script compliance and customer service. The goal of QA in any call center is to maintain high levels of service quality, ensure agents adhere to company policies and scripts, and identify areas of improvement.

article thumbnail

Accelerate NLP inference with ONNX Runtime on AWS Graviton processors

AWS Machine Learning

We also demonstrate the resulting speedup through benchmarking. Benchmark setup We used an AWS Graviton3-based c7g.4xl 1014-aws kernel) The ONNX Runtime repo provides inference benchmarking scripts for transformers-based language models. The scripts support a wide range of models, frameworks, and formats.

Benchmark 130
article thumbnail

Accelerated PyTorch inference with torch.compile on AWS Graviton processors

AWS Machine Learning

You can see that for the 45 models we benchmarked, there is a 1.35x latency improvement (geomean for the 45 models). You can see that for the 33 models we benchmarked, there is around 2x performance improvement (geomean for the 33 models). We benchmarked 45 models using the scripts from the TorchBench repo.

Benchmark 115