Remove 2012 Remove Big data Remove Examples Remove Personalization
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Bank Branch Innovation Like Never Before: 5 Brands Redefining Tradition

Avaya

When evaluating an institution for a loan, for example, 64% of customers prefer speaking to someone in person or over the phone. For example, the company has developed new ATM machines that can conduct card-less transactions using smartphone PIN codes. There’s no denying that banking is evolving.

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The IoT Chronicles Part 2: Three Big Security Threats—and How to Solve Them

Avaya

Having said that, here are three massive IoT security threats we’re seeing today (and how to expertly address them): Personally-owned devices: Research shows that about 40% of U.S. In fact, research shows that about 90% of all data in the world today was created in just the past few years (2.5

APIs 72
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Halo Smart Labs Develops a Smarter Smoke Alarm: IoT at It’s Best

Natalie Petouhof

. • Inability of traditional smoke detectors to connect to data centers about weather issues such as tornados, earthquakes, and floods. It was created in 2012 after a brush with tragedy. Inability of existing smoke detectors to deliver alerts when the house power connection is down. Why was Halo Smart Labs created?

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Top 5 Technology Trends in 2023

IdeasUnlimited

The idea of using big data to program software is not new. Research shows that almost 97% of mobile users are using these voice-based personal assistants that are based on AI. Home appliances that can be controlled by a smartphone, smartwatches that track your daily activity, and self-driving cars are all examples of IoT.

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Large-scale feature engineering with sensitive data protection using AWS Glue interactive sessions and Amazon SageMaker Studio

AWS Machine Learning

The policies are defined in a central location, allowing multiple analytics and ML services, such as AWS Glue, Amazon Athena , and SageMaker, to interact with data stored in Amazon S3. The dataset also includes sensitive information like personal phone numbers. For more information, see Setting up AWS Lake Formation.

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Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

AWS Machine Learning

A public GitHub repo provides hands-on examples for each of the presented approaches. Studio notebooks are designed to support you in all phases of your ML development, for example, ideation, experimentation, and operationalization of an ML workflow. Define a Dockerfile. For instructions, refer to Clean up.

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Buddying Up – Putting Virtual Employee Assistants at the Heart of Agent Development

TechSee

For example, Amazon’s Alexa for Business helps employees delegate tasks, while Nokia’s MIKA helps agents find answers as they perform complicated tasks or diagnose problems. For example, VEAs might combine visual customer assistance with agent decision support tools, motivation and career guidance.