MicroAI AtomML for RaspberryPi | APM Tutorial
Training AI on the Edge with MicroAI AtomML™
Videos
27020
post-template-default,single,single-post,postid-27020,single-format-standard,bridge-core-1.0.4,mec-dark-mode,translatepress-en_US,bridge,mega-menu-top-navigation,ajax_fade,page_not_loaded,,qode_grid_1400,qode-content-sidebar-responsive,qode-theme-ver-18.0.6,qode-theme-onetech,disabled_footer_bottom,wpb-js-composer js-comp-ver-5.7,vc_responsive
 

MicroAI AtomML for RaspberryPi | APM Tutorial

MicroAI AtomML for RaspberryPi | APM Tutorial

MicroAI™ Atom for Raspberry Pi | APM Tutorial
MicroAI™: MicroAI™ is an AI engine that can operate on low power edge devices. It can learn the pattern of any and all time series data and can be used to detect anomalies or abnormalities and make one step ahead predictions/forecasts.
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 raspberry pi device in order to monitor activity or entity.
This video shows MicroAI™ Atom for Raspberry Pi. MicroAI™ can currently be supported on raspberry pi 3. For APM, you will need some form of outside data from sensors. We recommend using the Raspberry Pi Sense HAT.
All the environment prep assets will need to be installed on the device (raspberry pi) directly. Any asset being installed on a device other than the raspberry pi (a laptop for instance) will be specifically identified. The easiest way to get your hands on MicroAI™ and start building is to prep your environment properly by using pip. Be sure that the latest version of pip is installed (pip3), as you will be using this to install all other assets involved in the environment prep.

youtube WATCH IT ON YouTube