Build machine learning models that learn and update on the fly!

Data changes fast in the real world, and traditional batch machine learning models often become stale as soon as they’re deployed. Updating them with the most current records can be tedious and expensive, making it more likely you’ll identify shifts and trends after the fact rather than responding to them as they happen. Real-Time Machine Learning shows you how to build ML systems that digest new data as it arrives, delivering accurate and relevant predictions even in environments where data changes rapidly.

In Real-Time Machine Learning you’ll learn how to:

• Ingest real-time data for online models
• Build real-time ML models with Python
• Monitor the performance of real-time ML models
• Real-time recommender systems, anomaly detection, and NLP

Real-Time Machine Learning reveals how to build models that learn while they operate in production environments. You’ll learn to use event-driven methodologies for real-time data processing and integrate online machine learning models into real-time data pipelines for responsive and adaptive applications.