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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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Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

AWS Machine Learning

Organizations across industries such as retail, banking, finance, healthcare, manufacturing, and lending often have to deal with vast amounts of unstructured text documents coming from various sources, such as news, blogs, product reviews, customer support channels, and social media. Extract and analyze data from documents.

Scripts 73
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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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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
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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. To access the code and documentation, refer to the GitHub repo. For more information, refer to Lower Numerical Precision Deep Learning Inference and Training.

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Simplify continuous learning of Amazon Comprehend custom models using Comprehend flywheel

AWS Machine Learning

Amazon Comprehend is a managed AI service that uses natural language processing (NLP) with ready-made intelligence to extract insights about the content of documents. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document.

APIs 69
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Create a document lake using large-scale text extraction from documents with Amazon Textract

AWS Machine Learning

AWS customers in healthcare, financial services, the public sector, and other industries store billions of documents as images or PDFs in Amazon Simple Storage Service (Amazon S3). In this post, we focus on processing a large collection of documents into raw text files and storing them in Amazon S3.

Scripts 92