ML for IoT Enabling Intelligent Connected Devices: Use ML to power real-time IoT decision-making systems
Devices that decide in milliseconds change everything. ML for IoT: Enabling Intelligent Connected Devices shows how to turn raw sensor signals into real-time, on-device decisions that are robust, secure, and ready for scale. No fluff-just practical patterns that ship.
You'll build the full path from sensor to insight to action: streaming ingestion, feature pipelines, TinyML and edge inference, vision on low-power cameras, anomaly detection for equipment, and prediction services that hold up under network hiccups and noisy data. You'll learn when to process on the device, at the gateway, or in the cloud-and how to keep models updated safely with observability, A/B testing, and rollbacks.
What you'll be able to do:
Design event-driven IoT architectures that support sub-second ML decisions
Engineer sensor features for vibration, audio, vision, and environmental data
Deploy lightweight deep learning and classical models on MCUs, SBCs, and gateways
Build predictive maintenance and anomaly detection that actually reduce downtime
Optimize for latency, accuracy, and power with quantization and model compression
Operate at scale with data engines, OTA updates, MLOps, monitoring, and security best practices
Written for engineers, architects, and product builders, this book is your blueprint for intelligent, connected products that are fast, dependable, and field-proven.
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