The Definitive Guide to Streaming Machine Learning

How to Apply Machine Learning to Streaming Data

The Eckerson Group guide to streaming ML

Radicalbit’s mission is to design cutting-edge products that integrate event stream processing and artificial intelligence, seamlessly uniting people, processes, and technologies.

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Due to the need for quick decision-making in various ML applications, machines must think rapidly after learning. Companies are to respond promptly to business needs such as predicting market trends, suggesting customer purchases, and customizing web pages interactions. Consequently, there is a growing trend towards streaming ML, which enables the application of ML models to streaming data.

happy data scientist
happy data scientist
happy data scientist

This free guide offers a comprehensive exploration of the technologies, processes, and principles underlying streaming ML and includes practical examples of how it can be applied to real-life business challenges. The guide is an essential resource for data practitioners and leaders seeking to develop successful real-time ML programs that provide value to their businesses.

This guide is sponsored by Radicalbit and authored by Kevin Petrie, VP of Research at Eckerson Group, a global research, consulting, and advisory firm specializing in data strategy, data architecture, self-service analytics, master data management, data governance, and data science.

What you'll find in the white paper

The free Eckerson Guide to Streaming ML caters to professionals like CIOs, CTOs, Data Scientists, ML Engineers, Data Engineers and covers the following topics:

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Introduction to ML, streaming ML and architectural components through examples

Identification and management of data features, training and operating streaming ML models

Guiding principles for companies to succeed with a streaming ML program

Business perspective on how streaming ML works, through two use cases: customer recommendations and fraud detection

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