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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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Provide live agent assistance for your chatbot users with Amazon Lex and Talkdesk cloud contact center

AWS Machine Learning

The integration of Amazon Lex with Talkdesk cloud contact center is inspired by WaFd Bank (WaFd)’s digital innovation journey to enhance customer experience. For example, the following figure shows screenshots of a chatbot transitioning a customer to a live agent chat (courtesy of WaFd Bank).

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Schneider Electric leverages Retrieval Augmented LLMs on SageMaker to ensure real-time updates in their ERP systems

AWS Machine Learning

This post was co-written with Anthony Medeiros, Manager of Solutions Engineering and Architecture for North America Artificial Intelligence, and Blake Santschi, Business Intelligence Manager, from Schneider Electric. In particular, they are routinely used to store information related to customer accounts.

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InformedIQ automates verifications for Origence’s auto lending using machine learning

AWS Machine Learning

The solution automatically extracts data and classifies documents (for example, driver’s license, paystub, W2 form, or bank statement), providing the required fields for the consumer verifications used to determine if the lender will grant the loan. Main advantages.

APIs 71
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Create powerful self-service experiences with Amazon Lex on Talkdesk CX Cloud contact center

AWS Machine Learning

The benefits of Amazon Lex and Talkdesk CX Cloud are exemplified by WaFd Bank , a full-service commercial US bank in 200 locations and managing $20 billion in assets. The bank has invested in a digital transformation of its contact center to provide exceptional service to its clients.

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Redact sensitive data from streaming data in near-real time using Amazon Comprehend and Amazon Kinesis Data Firehose

AWS Machine Learning

You previously had to rely on personally identifiable information (PII) rules engines that could flag false positives or miss data, or you had to build and maintain custom machine learning (ML) models to identify PII in your streaming data. Total cost for identifying log records with PII using ContainsPiiEntities API = $0.1

APIs 83
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Build and train ML models using a data mesh architecture on AWS: Part 1

AWS Machine Learning

Create accountability on data providers from individual LoBs to share curated data assets that are discoverable, understandable, interoperable, and trustworthy. In this first post, we show the procedures of setting up a data mesh architecture with multiple AWS data producer and consumer accounts. Financial services use case.