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Big Data Boom: How to Capture and Leverage Insights

The Northridge Group

To make it worse, the pace of this information continues to increase; data (lots of it) is being produced more rapidly than ever before. How can you capture this data and use it to help you and your organization make informed decisions, and ideally to predict future behavior and events?

Big data 131
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Bundesliga Match Fact Win Probability: Quantifying the effect of in-game events on winning chances using machine learning on AWS

AWS Machine Learning

But what are the events that led to these surprising in-game swings of win probability? Win Probability consumes event data from an ongoing match (goal events, fouls, red cards, and more) as well as data produced by other Match Facts, such as xGoals. Tareq Haschemi is a consultant within AWS Professional Services.

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What Do The Pioneers of Customer Experience See for the Future: And What Should You Do About It?

Beyond Philosophy

It appeals to me because it uses a data-driven approach. Customer Science is a product of a perfect storm of artificial intelligence, the information provided by Big Data, and the interpretation of that data through Behavioral Science. appeared first on CX Consulting. Follow Colin Shaw on Twitter @ColinShaw_CX.

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International Women’s Day: A Closer Look at Some of the Women Who Drive NRG

The Northridge Group

Kelsey earned a degree in economics and later worked at a marketing agency before coming to NRG as a Consultant. Tressa has been with NRG in one capacity or another – consulting, process work, IT and program management – for 12 years. I felt a sense of relief.” And her son?

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Bundesliga Match Fact Pressure Handling: Evaluating players’ performances in high-pressure situations on AWS

AWS Machine Learning

The following heuristic is used for computing the escape rate: We start with a series of pressure events, based on the existing Most Pressed Player Match Fact. Each event consists of a list containing all individual pressure events on the ball carrier during one individual ball possession (IBP) phase. Examples of escapes.

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How Zalando optimized large-scale inference and streamlined ML operations on Amazon SageMaker

AWS Machine Learning

We explored multiple big data processing solutions and decided to use an Amazon SageMaker Processing job for the following reasons: It’s highly configurable, with support of pre-built images, custom cluster requirements, and containers. He enjoys traveling and reading about historical events. He is also a cycling enthusiast.

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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning

You can also use Amazon EventBridge to monitor events related to Amazon Bedrock. This allows you to create rules that invoke specific actions when certain events occur, enhancing the automation and responsiveness of your observability setup (for more details, see Monitor Amazon Bedrock ).