Remove Accountability Remove APIs Remove Calibration Remove Scripts
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Accelerate Amazon SageMaker inference with C6i Intel-based Amazon EC2 instances

AWS Machine Learning

Use the supplied Python scripts for quantization. Run the provided Python test scripts to invoke the SageMaker endpoint for both INT8 and FP32 versions. In this case, you are calibrating the model with the SQuAD dataset: model.eval() conf = ipex.quantization.QuantConf(qscheme=torch.per_tensor_affine) print("Doing calibration.")

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Auto-labeling module for deep learning-based Advanced Driver Assistance Systems on AWS

AWS Machine Learning

AV/ADAS teams need to label several thousand frames from scratch, and rely on techniques like label consolidation, automatic calibration, frame selection, frame sequence interpolation, and active learning to get a single labeled dataset. This includes scripts for model loading, inference handling etc.

APIs 80
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Detect fraudulent transactions using machine learning with Amazon SageMaker

AWS Machine Learning

To demonstrate how you can use this solution in your existing business infrastructures, we also include an example of making REST API calls to the deployed model endpoint, using AWS Lambda to trigger both the RCF and XGBoost models. If you don’t have an account, you can sign up for one. Prerequisites. Launch the solution.

APIs 66
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What Does Digital Transformation Look Like for Contact Centers?

pindrop

Process Automation – Intelligent call routing, intelligent scripting and unification of desktop across applications to improve agent efficiency. Fraudsters employ brute force tactics to extract account information and identity data out of the IVR and leverage that data across other channels to commit fraud. How can Pindrop help?