Real-Time Machine Learning (MEAP V03)

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.
about the book
In Real-Time Machine Learning you’ll build an understanding of real-time data, how to ingest it from real-time sources, and how to create recommenders, anomaly detection, and language models that learn from on-the-fly feedback. You won’t need complex or expensive cloud resources: all the examples in the book will run on a standard laptop! You’ll soon be delivering the kind of models favored by Netflix and other big enterprises for their accuracy and cost-effectiveness.