MicroAI AtomML for ESP32 | APM
Training AI on the Edge with MicroAI AtomML™
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MicroAI AtomML for ESP32 | APM

MicroAI™ Atom for ESP32 | APM Tutorial

MicroAI™: MicroAI™ is an AI engine that can operate on low power edge and endpoint devices. It can learn the pattern of any and all-time series data and can be used to detect anomalies or abnormalities, make one step ahead predictions/forecasts, and calculate the remaining life of entities (whether it is industrial machinery, small devices or the like).

This Video covers MicroAI as implemented in the APM use case. APM is defined below:
APM: APM stands for Asset Performance Management, but to put it simply, when we refer to APM what we are really saying is that we are attaching external sensors to our ESP32 device in order to monitor activity or entity.

MicroAI™ can be supported on any ESP32 based board. This SDK is tailored to the ESP-WROVER-KIT which provides an onboard Micro-SD card slot, LCD panel, and I/O expansion capabilities.
For APM, you will need some form of outside data from sensors. This SDK has been configured to interface with the following sensors:
• Grove – I2C High Accuracy Temperature Sensor – MCP9808
• Grove – 6-Axis Accelerometer & Gyroscope
• Grove – Sound Sensor Based on the LM358 amplifier
These are just examples, but any sensor that connects via the supported communication interfaces will work. Note that the provided code that reads these sensors is disabled by default to allow the user to get their system operational even without the sensors.

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