Internet of Things (IoT) has enabled customers in multiple industries, such as manufacturing, automotive, and energy, to monitor and control real-world environments. By deploying a variety of edge IoT devices such as cameras, thermostats, and sensors, you can collect data, send it to the cloud, and build machine learning (ML) models to predict anomalies, failures, and more. However, if the use case requires real-time prediction, you need to enrich your IoT solution with ML at the edge ([email protected]) capabilities. [email protected] is a concept that decouples the ML model’s lifecycle from the app lifecycle and allows you to run an end-to-end ML pipeline that includes data preparation, model building, model compilation and optimization, model deployment (to a fleet of edge devices), model execution, and model monitoring and governing. You deploy the app once and run the ML pipeline as many times as you need.
As you can imagine, to implement all

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