Remove APIs Remove Calibration Remove Engineering Remove Scripts
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What is Call Scripting and How To Create it?

NobelBiz

Writing a call script is a must for contact centers that want to excel in their prospecting effort. If you write it according to the rules of the game, the script is an observable, cost-effective, and efficient method of attracting and maintaining prospects and clients. What exactly is call scripting? Why do scripts exist?

Scripts 52
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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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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. This adds a useful calibration to our model. Prerequisites. Launch the solution.

APIs 67
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Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services

AWS Machine Learning

Evaluating these models allows continuous model improvement, calibration and debugging. Once in production, ML consumers utilize the model via application-triggered inference through direct invocation or API calls, with feedback loops to model owners for ongoing performance evaluation. name: "llama2-7b-finetuned".

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Run secure processing jobs using PySpark in Amazon SageMaker Pipelines

AWS Machine Learning

When processing large-scale data, data scientists and ML engineers often use PySpark , an interface for Apache Spark in Python. SageMaker provides prebuilt Docker images that include PySpark and other dependencies needed to run distributed data processing jobs, including data transformations and feature engineering using the Spark framework.