Michelle Foley Picture a world where healthcare is not confined to a clinic.

Microchip has announced the launch of a new software package designed to put machine learning workloads onto eight-, 16-, and 32-bit microcontrollers and processors: the MPLAB Machine Learning Development Suite. "Machine Learning is the new normal for embedded controllers, and utilizing it at the edge allows a product to be efficient, more secure and use less power than systems that rely on cloud communication for processing," claims Microchip's Rodger Richey of the core benefits behind on-device machine learning with resource-constrained hardware, known as "tinyML." Microchip's unique, integrated solution is designed for embedded engineers and is the first to support not just 32-bit MCUs and MPUs [Microcontroller Units and Microprocessor Units], but also 8- and 16-bit devices to enable efficient product development." Designed for use alongside the MPLAB X Integrated Development Environment (IDE), the machine learning toolkit allows the developer to build machine learning models suitable for flashing to Microchip's various microcontroller and processor parts taking into account their limited resources compared to desktop computers or cloud servers. Driven by AutoML and with the option to use cloud computing resources to find the best algorithm for a given task, the package aims to cover feature extraction, training, validation, and testing in one, with an application programming interface (API) convertible to Python

Here is why you should consider Python for Machine Learning and AI Python has emerged as the preferred programming language for machine learning and artificial intelligence (AI) applications. Its versatility, ease of use, and extensive library support make it the top choice for data scientists, researchers, and engineers working in these fields