Machine learning and AI are subsets of data science, but what determines the intersect? Most companies looking to implement AI are bound by the inherent limits of their industry. How does quick feedback impact our ability to develop models that are reliable, and what are the data science implications of developing models in such conditions? In this talk we'll introduce some basic concepts and answer these questions, to gain an understanding of when is AI easier and harder to implement, using lessons learned at Skyline AI, which augments large real estate investment vehicles with AI technology.
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