ML in Motion: Real-Time Models for Dynamic Worlds

Static models struggle in moving environments. ML in Motion is a machine learning book devoted to building real-time systems that learn, adapt, and respond as the world changes.
The writing explores machine learning under motion: streaming data, shifting distributions, time-sensitive decisions, and feedback loops. Models here are not trained once and frozen; they evolve with incoming signals and operational constraints.
Rather than focusing only on algorithms, the book emphasizes system design. Readers learn how latency, drift, infrastructure, and monitoring shape real-time performance. Each chapter shows how modeling choices must align with timing, reliability, and scale.
The tone is technical yet pragmatic, aimed at practitioners working with live systems. Language remains precise and actionable, translating dynamic challenges into architectural patterns and operational practices.
ML in Motion moves through online learning, streaming pipelines, drift detection, real-time inference, and feedback control—illustrating how intelligence performs when conditions never stand still.
Key themes explored include:
• Real-time machine learning
• Data streams and latency
• Concept drift and adaptation
• System-aware modeling
• Reliability under change
ML in Motion is for teams operating at speed—offering guidance to build models that work while the world keeps moving.