Remove Accountability Remove Examples Remove Metrics Remove Scripts
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How to Write an After-Call Survey Script

Fonolo

Customer satisfaction and net promoter scores are helpful metrics, but the after-call survey is the most immediate resource. The value is in the timing—customers will give the most accurate accounts of their service experiences shortly after they’ve happened. Sample After-Call Survey Script. What is an After-Call Survey For?

Scripts 138
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Build a cross-account MLOps workflow using the Amazon SageMaker model registry

AWS Machine Learning

When designing production CI/CD pipelines, AWS recommends leveraging multiple accounts to isolate resources, contain security threats and simplify billing-and data science pipelines are no different. Some things to note in the preceding architecture: Accounts follow a principle of least privilege to follow security best practices.

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The Case For the Anti-Script: A Multifactor Analysis of Script Adherence

Balto

“The anti-script doesn’t mean that you should wing it on every call… what anti-script means is, think about a physical paper script and an agent who is reading it off word for word… you’re taking the most powerful part of the human out of the human.” Share on Twitter. Share on Facebook.

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The pivotal role of outbound call scripts

NobelBiz

The first step toward running a successful campaign starts with creating a good outbound call script. The purpose behind outbound call scripts No matter who your prospects really are, one thing is certain. Hence the need for an outbound call script that follows certain golden rules. They will always impose a time limit.

Scripts 52
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Efficiently fine-tune the ESM-2 protein language model with Amazon SageMaker

AWS Machine Learning

For example, in 2023, a research team described training a 100 billion-parameter pLM on 768 A100 GPUs for 164 days! In the following sections, we go through the steps to prepare your training data, create a training script, and run a SageMaker training job. The following diagram illustrates this workflow. apply(lambda x: len(x)).between(100,

Scripts 93
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Implement a custom AutoML job using pre-selected algorithms in Amazon SageMaker Automatic Model Tuning

AWS Machine Learning

For example, it can scale the data, perform univariate feature selection, conduct PCA at different variance threshold levels, and apply clustering. For this example, you don’t use a specialized dataset; instead, you work with the California Housing dataset that you will import from Amazon Simple Storage Service (Amazon S3).

Scripts 87
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Amazon SageMaker with TensorBoard: An overview of a hosted TensorBoard experience

AWS Machine Learning

It provides a suite of tools for visualizing training metrics, examining model architectures, exploring embeddings, and more. TensorFlow and PyTorch projects both endorse and use TensorBoard in their official documentation and examples. is your training script, and simple_tensorboard.ipynb launches the SageMaker training job.

Scripts 74