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Previous software tools first designed to develop machine learning in academic research contexts are less-than-ideal for developing applied AI solutions for enterprise today. Here we presented at the Big Data & AI Toronto conference core attributes required for the next generation of software tools needed to expedite the application of AI in business and industry. We begin with an overview of our research where we assessed the needs of hundreds of AI professionals as well as completed prototypes and simulations of various applied machine learning projects. These findings guided our development of a novel development environment (the Zetane Engine) for advanced machine learning projects that is visual and less abstract. Our discussion will focus on why features of our software help better meet the needs of AI professionals in industry, focusing on issues related to:
* Better debugging of models and streamlined workflows;
* Facilitating collaboration and input from non-technical members within the business as well as oversight by domain experts such as clinicians for health-related projects;
* Strengthened abilities to simulate outputs and renderings from machine-learning models in order to better demonstrate AI innovations to clients and regulators.
Throughout this presentation, we will frame each topic within actual case studies and projects we completed with our enterprise clients in industries that include defense, transportation, biomedical research and more.