  We present a no-code Artificial Intelligence (AI) platform called Trinity
with the main design goal of enabling both machine learning researchers and
non-technical geospatial domain experts to experiment with domain-specific
signals and datasets for solving a variety of complex problems on their own.
This versatility to solve diverse problems is achieved by transforming complex
Spatio-temporal datasets to make them consumable by standard deep learning
models, in this case, Convolutional Neural Networks (CNNs), and giving the
ability to formulate disparate problems in a standard way, eg. semantic
segmentation. With an intuitive user interface, a feature store that hosts
derivatives of complex feature engineering, a deep learning kernel, and a
scalable data processing mechanism, Trinity provides a powerful platform for
domain experts to share the stage with scientists and engineers in solving
business-critical problems. It enables quick prototyping, rapid experimentation
and reduces the time to production by standardizing model building and
deployment. In this paper, we present our motivation behind Trinity and its
design along with showcasing sample applications to motivate the idea of
lowering the bar to using AI.
