Venturebeat reports that 87% of machine learning projects never make it to production, ever wondered why? Well, effectively deploying machine learning models is more of an art than science!
Over the past decade, we have witnessed a renaissance around Artificial Intelligence systems, a paradigm shift from computerized deduction to computerized induction. But the issue with these systems is that they are inherently complex, have fuzzy boundaries, rely heavily on data dependencies, and fundamentally change due to variation in the actual world data. Thus, adopting test-driven development in these systems could be tricky. In this talk, I will share my learnings on how to build effective practices to deploy machine learning models into production. Also, I will cover how to test the decisions made by the machine learning model using SOLID principles.