Remove Apache Kafka Remove Events Remove ML
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Streaming Machine Learning Without a Data Lake

ODSC - Open Data Science

Be sure to check out his talk, “ Apache Kafka for Real-Time Machine Learning Without a Data Lake ,” there! The combination of data streaming and machine learning (ML) enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem.

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Real-time artificial intelligence and event processing  

IBM Journey to AI blog

By leveraging AI for real-time event processing, businesses can connect the dots between disparate events to detect and respond to new trends, threats and opportunities. AI and event processing: a two-way street An event-driven architecture is essential for accelerating the speed of business.

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Building a Business with a Real-Time Analytics Stack, Streaming ML Without a Data Lake, and…

ODSC - Open Data Science

Building a Business with a Real-Time Analytics Stack, Streaming ML Without a Data Lake, and Google’s PaLM 2 Building a Pizza Delivery Service with a Real-Time Analytics Stack The best businesses react quickly and with informed decisions. Here’s a use case of how you can use a real-time analytics stack to build a pizza delivery service.

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Anomaly detection in streaming time series data with online learning using Amazon Managed Service for Apache Flink

AWS Machine Learning Blog

In this post, we demonstrate how to build a robust real-time anomaly detection solution for streaming time series data using Amazon Managed Service for Apache Flink and other AWS managed services. This solution employs machine learning (ML) for anomaly detection, and doesn’t require users to have prior AI expertise.

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How Thomson Reuters delivers personalized content subscription plans at scale using Amazon Personalize

AWS Machine Learning Blog

The key requirement for TR’s new machine learning (ML)-based personalization engine was centered around an accurate recommendation system that takes into account recent customer trends. ML training pipeline. TR customer data is changing at a faster rate than the business rules can evolve to reflect changing customer needs.

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Streaming Data Pipelines: What Are They and How to Build One

Precisely

More than ever, advanced analytics, ML, and AI are providing the foundation for innovation, efficiency, and profitability. Streaming data pipelines, by extension, offer an architecture capable of handling large volumes of data, accommodating millions of events in near real time. The concept of streaming data was born of necessity.

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Bundesliga Match Facts Shot Speed – Who fires the hardest shots in the Bundesliga?

AWS Machine Learning Blog

This process comprises two key components: event data and optical tracking data. Event data collection entails gathering the fundamental building blocks of the game. For the precision needed in shot speed calculations, we must ensure that the ball’s position aligns precisely with the moment of the event.

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