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

AWS Machine Learning

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. 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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Top Banking Issues Today For Keeping Up With Customer Expectations

Integrity Solutions

The banking industry is undergoing a significant transformation, driven by technological advancements, changing customer expectations and evolving regulatory landscapes. Incidents involving banks like SVB and First Republic Bank, coupled with the emergence of new banks, have placed many regional and community banks in a precarious position.

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Accelerate client success management through email classification with Hugging Face on Amazon SageMaker

AWS Machine Learning

This is a guest post from Scalable Capital , a leading FinTech in Europe that offers digital wealth management and a brokerage platform with a trading flat rate. Solution overview Scalable Capital’s ML infrastructure consists of two AWS accounts: one as an environment for the development stage and the other one for the production stage.

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

AWS Machine Learning

You’ll see how to use Amazon SageMaker Edge Manager , Amazon SageMaker Studio , and AWS IoT Greengrass v2 to create an MLOps (ML Operations) environment that automates the process of building and deploying ML models to large fleets of edge devices. In this case, the production environment isn’t a SageMaker model endpoint but an edge device.

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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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Unlock the potential of generative AI in industrial operations

AWS Machine Learning

Workers gain productivity through AI-generated insights, engineers can proactively detect anomalies, supply chain managers optimize inventories, and plant leadership makes informed, data-driven decisions. For details, refer to Step 1: Create your AWS account. For this tutorial, you need a bash terminal with Python 3.9

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Customer Service Training: Empowering A Service Mindset

Integrity Solutions

It’s a never-ending process that we need to nurture, foster and manage. In the banking sector this is especially apparent. They don’t want ten minutes of on-hold music only to hear canned, scripted responses that ignore their real issues and needs. But great customer experiences don’t just happen. Really heard.