Remove Accountability Remove Construction Remove Scripts
article thumbnail

Here’s How to Write Effective Call Center Scripts

Quality Contact Solutions

After writing over one thousand call center scripts, we know that there isn’t a single stand-alone ingredient we’d consider the ‘secret sauce’ for creating the perfect script. Instead, scripts are purposeful and serve as a guide to accomplish the objective of the call. No, it doesn’t.

Scripts 98
article thumbnail

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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

Live Chat Scripts for Sales and Customer Service

ProProfs Blog

What makes live chat scripts so important for sales and customer service? To realize all the benefits of live chat scripts, you need to understand the importance of chat etiquette for your customers’ experience and satisfaction. Useful Customer Service Scripts Templates And Examples. Customer Service Greetings Scripts.

Scripts 133
article thumbnail

Use the AWS CDK to deploy Amazon SageMaker Studio lifecycle configurations

AWS Machine Learning

Lifecycle configurations are shell scripts triggered by Studio lifecycle events, such as starting a new Studio notebook. AWS CDK constructs are the building blocks of AWS CDK applications, representing the blueprint to define cloud architectures. AWS CDK constructs The file we want to inspect is aws_sagemaker_lifecycle.py.

article thumbnail

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Mitigation strategies : Implementing measures to minimize or eliminate risks.

article thumbnail

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

AWS Machine Learning

The first allows you to run a Python script from any server or instance including a Jupyter notebook; this is the quickest way to get started. The second approach is a turnkey deployment of various infrastructure components using AWS Cloud Development Kit (AWS CDK) constructs. We have packaged this solution in a.ipynb script and.py

Scripts 103
article thumbnail

Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

AWS Machine Learning

One important aspect of this foundation is to organize their AWS environment following a multi-account strategy. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs.

APIs 77