Towards Tool-Support for Interactive-Machine Learning Applications in the Android Ecosystem

TL;DR

This paper explores the development of tools to support AI engineers in creating, testing, and deploying machine learning models across device ecosystems, focusing on smart consumer applications on Android devices.

Contribution

It introduces preliminary insights and proposed features for tool-support to address challenges in implementing interactive machine learning in device ecosystems.

Findings

Identified key challenges through interviews with AI engineers

Developed proposed features like sensor data collection and resource testing

Conducted experiments on a Smartwatch and Smartphone for an interactive ML use case

Abstract

Consumer applications are becoming increasingly smarter and most of them have to run on device ecosystems. Potential benefits are for example enabling cross-device interaction and seamless user experiences. Essential for today's smart solutions with high performance are machine learning models. However, these models are often developed separately by AI engineers for one specific device and do not consider the challenges and potentials associated with a device ecosystem in which their models have to run. We believe that there is a need for tool-support for AI engineers to address the challenges of implementing, testing, and deploying machine learning models for a next generation of smart interactive consumer applications. This paper presents preliminary results of a series of inquiries, including interviews with AI engineers and experiments for an interactive machine learning use case with a Smartwatch and Smartphone. We identified the themes through interviews and hands-on experience working on our use case and proposed features, such as data collection from sensors and easy testing of the resources consumption of running pre-processing code on the target device, which will serve as tool-support for AI engineers.