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Lesson #17 Revisited: The Survey Is Dead. Long Live the Survey! (And AI is Helping)

PeopleMetrics

Even the most sophisticated AI models need to be calibrated and continually reviewed by people who understand the cultural context of your customer base. Its up to you to decide which questions align with your strategic goals. Ensuring Cultural Fit : Every industry has its own language and nuances.

Surveys 104
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Cutting Through the Buzzwords of AI in the Contact Center

CCNG

Analytical AI is the fuel that drives the AI engine for contact centers. Implementing one solution at a time allows for proper calibration of that solution and gives you the ability to feel the full ramifications of that technology without any guesswork. The more information you feed it, the better your operations will become.

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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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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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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.