Remove Accountability Remove Banking Remove Engineering Remove Scripts
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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).

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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 .

Scripts 79
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Everything You Need to Know About Auto Attendant

Hodusoft

In this blog post, we will discuss everything about auto attendants starting from what they are, how they work, the pros and cons of using auto attendants, how to set up an auto attendant, and how to include scripts. Pros of Using Auto Attendant Cons of Auto Attendant Auto Attendant Scripts – What to Record? Read on to know more.

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Build and train ML models using a data mesh architecture on AWS: Part 2

AWS Machine Learning

In part 1 , we addressed the data steward persona and showcased a data mesh setup with multiple AWS data producer and consumer accounts. The data scientists in this team use Amazon SageMaker to build and train a credit risk prediction model using the shared credit risk data product from the consumer banking LoB. Data exploration.

Scripts 71
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Build custom code libraries for your Amazon SageMaker Data Wrangler Flows using AWS Code Commit

AWS Machine Learning

It contains over 300 built-in data transformation steps to aid with feature engineering, normalization, and cleansing to transform your data without having to write any code. For this post, we use the bank-full.csv data from the University of California Irving Machine Learning Repository to demonstrate these functionalities.

Scripts 62
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How identity-interrogation impacts your customer relationships

TRUSTID

But before the agent asks about why they called, they start the conversation by following a lengthy script filled with a bunch of personal questions that the caller must correctly answer. You’ve been banking with the financial institution for years. You feel a sense of pride of who you bank with. How would this make you feel?

Banking 54
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MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass

AWS Machine Learning

Productionization of robust ML models requires the collaboration of multiple personas, such as data scientists, ML engineers, data engineers, and business stakeholders, under a semi-automate infrastructure following specific operations (MLOps). Solution overview.