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The chapter explores the critical role of Machine Learning (ML) in the Internet of Things (IoT), detailing the ML pipeline from data collection to deployment. It highlights key applications such as time-series forecasting, anomaly detection, and predictive maintenance, emphasizing the necessity for lightweight tools and frameworks tailored for resource-constrained IoT devices. Moreover, it discusses the challenges of implementing ML in IoT environments, such as data quality and model updating.
Chapter 628 sections
The chapter explores the critical role of Machine Learning (ML) in the Internet of Things (IoT), detailing the ML pipeline from data collection to deployment. It highlights key applications such as time-series forecasting, anomaly detection, and predictive maintenance, emphasizing the necessity for lightweight tools and frameworks tailored for resource-constrained IoT devices. Moreover, it discusses the challenges of implementing ML in IoT environments, such as data quality and model updating.
The ML pipeline transforms raw IoT data into actionable insights through various stages including data collection, preprocessing, model training, validation, deployment, and monitoring.
Key applications of ML in IoT include predictive maintenance, which prevents equipment failures, and anomaly detection, which identifies deviations from normal behavior.
Lightweight ML frameworks like TensorFlow Lite and Edge Impulse are essential for running efficient models on devices with limited resources.
A structured process through which raw data is converted into actionable insights using machine learning techniques, including collection, preprocessing, training, testing, and deployment.
The identification of data points that fall outside the expected range of normal behavior, often used in predictive maintenance to prevent failure.
A proactive maintenance strategy that involves predicting equipment failures and scheduling maintenance activities based on selected data patterns.
Artificial intelligence processing done locally on edge devices, which reduces latency, conserves bandwidth, and enhances privacy by processing data without needing to send it to the cloud.
Total Questions
2
Estimated Time
4 min
Passing Score
70%
1 more question available