1344  Modeling and Inferencing for IoT

1344.1 Overview

Machine learning transforms raw IoT sensor data into actionable insights—detecting activities, predicting failures, and enabling intelligent automation. This chapter series covers the complete ML lifecycle from data collection through production deployment.

Figure 1344.1: The data science pipeline for IoT follows a systematic progression from raw sensor streams to deployed models.

1344.2 Chapter Series

This topic has been organized into seven focused chapters for easier navigation:

Chapter	Title	Key Topics	Difficulty
1	ML Fundamentals	Training vs inference, feature extraction, edge vs cloud	Beginner
2	Mobile Sensing & Activity Recognition	HAR, transportation mode detection, duty cycling	Intermediate
3	IoT ML Pipeline	7-step pipeline, data leakage, model selection	Intermediate
4	Edge ML & TinyML Deployment	Quantization, pruning, HVAC predictive control	Intermediate
5	Audio Feature Processing	MFCC extraction, wake word detection	Intermediate
6	Feature Engineering	Good vs bad features, domain knowledge	Intermediate
7	Production ML	Monitoring, anomaly detection, predictive maintenance	Advanced

1344.3 Learning Path

1344.3.1 For Beginners

Start with ML Fundamentals to understand:

• What machine learning does for IoT
• The difference between training and inference
• Why feature extraction matters
• When to use edge vs cloud ML

1344.3.2 For Practitioners

Follow the complete pipeline:

1. Mobile Sensing - Real-world activity recognition
2. IoT ML Pipeline - Systematic 7-step approach
3. Edge Deployment - TinyML and quantization
4. Feature Engineering - Designing discriminative features

1344.3.3 For Production Engineers

Focus on deployment and operations:

• Edge ML & Deployment - Model optimization for constrained devices
• Production ML - Monitoring, drift detection, anomaly detection

1344.4 Key Concepts Summary

1344.4.1 The IoT ML Pipeline

%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#2C3E50', 'primaryTextColor': '#fff', 'primaryBorderColor': '#16A085', 'lineColor': '#16A085', 'secondaryColor': '#E67E22', 'tertiaryColor': '#ecf0f1'}}}%%
flowchart LR
    S1[Data<br/>Collection] --> S2[Data<br/>Cleaning]
    S2 --> S3[Feature<br/>Engineering]
    S3 --> S4[Train/Test<br/>Split]
    S4 --> S5[Model<br/>Selection]
    S5 --> S6[Evaluation]
    S6 --> S7[Deployment]

    style S1 fill:#2C3E50,stroke:#16A085,color:#fff
    style S3 fill:#E67E22,stroke:#2C3E50,color:#fff
    style S5 fill:#16A085,stroke:#2C3E50,color:#fff
    style S7 fill:#27AE60,stroke:#2C3E50,color:#fff

1344.4.2 Edge vs Cloud Decision

Factor	Choose Edge	Choose Cloud
Latency	< 100ms required	1-5 seconds acceptable
Privacy	Sensitive data	Anonymous data
Connectivity	Intermittent	Always connected
Model Size	< 1MB	> 10MB

1344.4.3 Feature Engineering Priority

Feature engineering contributes more to accuracy than algorithm choice:

1. Domain knowledge (physics-based features) > generic statistics
2. Time-domain features (mean, variance) are cheap and effective
3. Frequency-domain features (FFT) add 5-10% accuracy for periodic signals
4. Correlation analysis removes redundant features

1344.5 Related Topics

• Data Storage and Databases - Foundation for ML training pipelines
• Multi-Sensor Data Fusion - Combining multiple sensor streams
• Stream Processing - Real-time data processing
• Edge and Fog Computing - Deployment architectures

1344.6 Videos

NoteVideo: How to Benefit from Predictive Maintenance

NoteVideo: Predictive Maintenance Solutions

NoteVideo: Predictive Maintenance with ThingSpeak

1344.7 What’s Next

Begin with ML Fundamentals if you’re new to IoT machine learning, or jump to a specific chapter based on your needs.