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In this tutorial, we will take a fresh stream of noisy motor temperature telemetry, scrub it using Python and Pandas, hand the clean data off to a frontier AI model via API for automated pattern analysis

In a perfect world, sensors provide a continuous stream of clean, predictable measurements. On a real plant floor, the physical environment is hostile. Electromagnetic interference, transient voltage drops, and packet drops turn telemetry streams into ragged logs littered with missing timestamps, phantom spikes, and missing payloads.

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This time we'll keep that pipeline and change two things. Instead of temperature, we'll monitor whether our factory motor is running or stopped. Then we'll get an AI to evaluate the motor's operating pattern.

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A factory motor produces a temperature reading. A Raspberry Pi Pico W reads that sensor, adds a timestamp and passes the observation across the network.

We'll start with another dirty application-log dataset, clean it with Pandas, and then send the cleaned records to an AI model for an evaluation of what is happening in the application.