Real-time machine learning refers to the application of machine learning algorithms that continuously learn from incoming data and make predictions or decisions in real-time. Unlike batch machine learning, where data is collected over a period and processed in batches offline, real-time ML operates instantaneously on streaming data, allowing for immediate responses to changes or events.
Common use cases include fraud detection in financial transactions, predictive maintenance in manufacturing, recommendation systems in e-commerce, and personalized content delivery in media. Challenges in building real-time ML capabilities include managing high volumes of streaming data efficiently, ensuring low latency for timely responses, maintaining model accuracy and performance over time, and addressing privacy and security concerns associated with real-time data processing. This article delves into these concepts and provides insights into how organizations can overcome these challenges to deploy effective real-time ML systems.