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How To Set Up Excellent Call Center Calibration sessions?

NobelBiz

Calibration sessions serve this purpose for call centers. This article decodes the function and best practices for call calibration. Key Points: Call Center Calibration measures how well the call center works as a whole. You must assist the call center in ensuring the accuracy of its quality measurement procedures.

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Improving air quality with generative AI

AWS Machine Learning

The attempt is disadvantaged by the current focus on data cleaning, diverting valuable skills away from building ML models for sensor calibration. The data is then stored in Amazon S3 (Step 10) and can be published to OpenAQ so other organizations can use the calibrated air quality data.

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LiDAR 3D point cloud labeling with Velodyne LiDAR sensor in Amazon SageMaker Ground Truth

AWS Machine Learning

With a combination of optimal power and high performance, this sensor provides distance and calibrated reflectivity measurements at all rotational angles. Calibration for LiDAR vehicle 5-DOF extrinsic calibration (z is not observable). Calibration for LiDAR camera extrinsic, intrinsic, and distortion parameters.

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Accelerate Amazon SageMaker inference with C6i Intel-based Amazon EC2 instances

AWS Machine Learning

Import intel extensions for PyTorch to help with quantization and optimization and import torch for array manipulations: import intel_extension_for_pytorch as ipex import torch Apply model calibration for 100 iterations. Aniruddha Kappagantu is a Software Development Engineer in the AI Platforms team at AWS.

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Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

AWS Machine Learning

Data preprocessing and feature engineering First, the tracking data was filtered for just the data related to punts and kickoff returns. The data preprocessing and feature engineering was adapted from the winner of the NFL Big Data Bowl competition on Kaggle. The data distribution for punt and kickoff are different.

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Boost inference performance for Mixtral and Llama 2 models with new Amazon SageMaker containers

AWS Machine Learning

Be mindful that LLM token probabilities are generally overconfident without calibration. TensorRT-LLM requires models to be compiled into efficient engines before deployment. We need the following key parameters: engine – Specifies the runtime engine for DJL to use. For more details, refer to the GitHub repo.

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Face-off Probability, part of NHL Edge IQ: Predicting face-off winners in real time during televised games

AWS Machine Learning

Based on 10 years of historical data, hundreds of thousands of face-offs were used to engineer over 70 features fed into the model to provide real-time probabilities. By continuously listening to NHL’s expertise and testing hypotheses, AWS’s scientists engineered over 100 features that correlate to the face-off event.