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

AWS Machine Learning

For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics. SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. Features are inputs to ML models used during training and inference.

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Use your own training scripts and automatically select the best model using hyperparameter optimization in Amazon SageMaker

AWS Machine Learning

This post shows how Amazon SageMaker enables you to not only bring your own model algorithm using script mode, but also use the built-in HPO algorithm. We walk through the following steps: Use SageMaker script mode to bring our own model on top of an AWS-managed container. We use the MNIST dataset for this example.

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Same Tactics, Different Scripts: What Contact Center Fraud Sounds Like in the Age of Coronavirus

pindrop

With verified account numbers and some basic information, a fraudster has all they need to execute fraud through the phone channel using convincing scripts involving the current crisis to socially engineer contact center agents and individuals. . The New Fraud Scripts. Travel-Related Inconveniences and Emergencies .

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Call Scripts: 6 Golden Rules to Satisfy Your Customers

VocalCom

Call scripts help agents feel prepared when customers call your brand for service. Here are six golden rules for creating call scripts that satisfy your customers’ needs while still providing a gentle human touch. Abandon the script when necessary. Test call scripts regularly. Inform customers when there is a pause.

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6 Killer Applications for Artificial Intelligence in the Customer Engagement Contact Center

If Artificial Intelligence for businesses is a red-hot topic in C-suites, AI for customer engagement and contact center customer service is white hot. This white paper covers specific areas in this domain that offer potential for transformational ROI, and a fast, zero-risk way to innovate with AI.

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Use Snowflake as a data source to train ML models with Amazon SageMaker

AWS Machine Learning

After the data is downloaded into the training instance, the custom training script performs data preparation tasks and then trains the ML model using the XGBoost Estimator. Store your Snowflake account credentials in AWS Secrets Manager. Ingest the data in a table in your Snowflake account.

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Train and deploy ML models in a multicloud environment using Amazon SageMaker

AWS Machine Learning

For example, you might have acquired a company that was already running on a different cloud provider, or you may have a workload that generates value from unique capabilities provided by AWS. For a complete list of the pre-built Docker images managed by SageMaker, see Docker Registry Paths and Example Code. The Azure CLI.

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