The field of Machine Learning (ML) in the last decade has seen a boom in applications in different sectors of applied science, social sciences, business, and economics. ML is difficult and finding the best algorithm requires a lot of experience, energy, and intuition from the user. As the complexity of ML increases due to several factors, in the last few years, there has been a surge of AutoML. Automatic ML has as a goal to simplify as much as possible the user interaction with the underlying ML algorithms. Just to make an example, several BI applications such as Tableau, Power BI, etc., in many cases perform ML algorithms just with very few clicks by minimising the user input.

The field of Machine Learning (ML) in the last decade has seen a boom in applications in different sectors of applied science, social sciences, business, and economics. ML is difficult and finding the best algorithm requires a lot of experience, energy, and intuition from the user. As the complexity of ML increases due to several factors, in the last few years, there has been a surge of AutoML. Automatic ML has as a goal to simplify as much as possible the user interaction with the underlying ML algorithms. Just to make an example, several BI applications such as Tableau, Power BI, etc., in many cases perform ML algorithms just with very few clicks by minimising the user input.

The BrainCraft HAT has a 240×240 TFT IPS display for inference output, slots for camera connector cable for imaging projects, a 5 way joystick and button for UI input, left and right microphones, stereo headphone out, stereo 1 W speaker out, three RGB DotStar LEDs, two 3 pin STEMMA connectors on PWM pins so they can drive NeoPixels or servos, and Grove/STEMMA/Qwiic I2C port.