Real-Time Machine Learning (MEAP V03) | Real-Time Machine Learning

A Machine Learning Book for Systems That Cannot Wait

Most machine learning workflows assume that training data can be collected, cleaned, and frozen before a model goes live. Real-Time Machine Learning starts from a different premise: data keeps moving, conditions change, and models need to adapt as events arrive. In this MEAP V03 edition, Patrick Deziel and Prema Roman introduce online learning as a practical alternative to static batch training, then show how to build systems that ingest live data and generate timely predictions.

Why Online Learning Matters

The book opens by defining real-time data as data in motion and explaining why real-time is always a matter of acceptable latency. From there, it compares offline learning with online learning, showing how data drift can weaken models trained on historical datasets. The argument is straightforward: if the statistical properties of incoming data change, a model that only learns once may become outdated. Incremental learning offers a way to keep predictions relevant without repeatedly retraining from scratch.

What You’ll Build in MEAP V03

The early-access draft is organized around practical proof-of-concept systems. Readers work through event-driven data ingestion, then combine streaming data with online model training and inference. The projects include a nowcasting model, an anomaly detector, a real-time recommender system, and a language model that adapts to real-time feedback. The material also covers how to measure and monitor these systems as they operate.

Python Examples You Can Run

The examples are designed to run on a single machine using Python, making the concepts approachable for engineers who want to experiment before committing to a larger production stack. The emphasis stays on architecture and implementation: how data moves through the system, how a model learns from each new event, and how to reason about performance when the environment will not stand still.

Who This Early Access Edition Is For

This MEAP V03 is written for machine learning engineers and practitioners who want to build adaptive systems. It is especially relevant if you have worked with batch machine learning and now need to understand streaming data, online learning, or real-time inference. As an early-access manuscript, the contents reflect the book as it stood at V03, and later versions may expand or revise the material.

Start Building Systems That Learn as They Run

Real-Time Machine Learning is less about chasing the newest algorithm and more about changing how you think about training data, deployment, and adaptation. If your applications need to respond to events as they happen, this MEAP V03 draft offers a focused path into the architecture and code behind real-time machine learning.