Remove Calibration Remove Engineering Remove Scripts
article thumbnail

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
article thumbnail

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.")

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

Churn prediction using multimodality of text and tabular features with Amazon SageMaker Jumpstart

AWS Machine Learning

We now carry out feature engineering steps and then fit the model. The model training consists of two components: a feature engineering step that processes numerical, categorical, and text features, and a model fitting step that fits the transformed features into a Scikit-learn random forest classifier.

article thumbnail

Generate a counterfactual analysis of corn response to nitrogen with Amazon SageMaker JumpStart solutions

AWS Machine Learning

The causal inference engine is deployed with Amazon SageMaker Asynchronous Inference. The database was calibrated and validated using data from more than 400 trials in the region. For further details, refer to the feature extraction script. Initial nitrogen concentration in the soil was set randomly among a reasonable range.

article thumbnail

Use Stable Diffusion XL with Amazon SageMaker JumpStart in Amazon SageMaker Studio

AWS Machine Learning

It’s designed for professional use, and calibrated for high-resolution photorealistic images. offers SageMaker optimized scripts and container with faster inference time and can be run on smaller instance compared to the open weight SDXL 1.0. is the latest image generation model from Stability AI. Choose the SDXL 1.0

article thumbnail

Detect fraudulent transactions using machine learning with Amazon SageMaker

AWS Machine Learning

We also created a Python script that makes HTTP inference requests to the REST API, with our test data as input data. This adds a useful calibration to our model. Prior to joining AWS, she was a tech lead and senior full-stack engineer building data-intensive distributed systems on the cloud. file in the solution’s source code.

APIs 72
article thumbnail

25 Call Center Leaders Share the Most Effective Ways to Boost Contact Center Efficiency

Callminer

Bill Dettering is the CEO and Founder of Zingtree , a SaaS solution for building interactive decision trees and agent scripts for contact centers (and many other industries). Interactive agent scripts from Zingtree solve this problem. Agents can also send feedback directly to script authors to further improve processes.