Machine learning and deep learning have become standard tools in a data scientist’s toolbox, applied to generate insights into large amounts of data. But organizations are realizing that these technologies can’t do it all, and when decision making needs to be fast, reliable, and defendable, mathematical optimization is perfect complimentary tool to machine learning.
Predictive models help answer the question of “What is likely to happen?” but also answering “What actions should be taken?” will allow organizations to add depth and value to their analytics projects where others can’t.
Presented by:
Jerry Yurchisin | Data Science Strategist | Gurobi
