Introduction for Data Scientists

Why is MIP important to data scientists?

Simply put – Mixed-Integer Programming (MIP) answers questions that Machine Learning (ML) cannot.  Incorporating MIP into your data science repertoire opens many more applications up to you and increases your impact on the business.  The techniques of MIP were invented many years ago, but recent advances in computing poweralgorithms, and data availability have made it possible to handle the world’s most complex business problems at speed.  As a result, MIP has had a massive impact on a wide variety of business areas. 

Why should you use Mixed Integer Programming (MIP)?

In this video, Gurobi CEO and Co-founder Ed Rothberg explains how MIP combines expressiveness and robustness to produce high-quality, reliable solutions.


How does mixed-integer programming work in concert with machine learning techniques?

ML makes predictions while MIP makes decisions. When your problem involves complex tradeoffs between competing activities and allows for trillions of possible solutions, only MIP has the power to find the best or optimal one. MIP is often complementary to ML. For example, instead of using just ML to decide which offer goes in front of which web customer, you can marry ML to MIP to choose a set of offers that drives the greatest profitability. Or consider predictive maintenance (e.g., elevator repair). ML can predict when certain types of failures are likely to occur, and MIP can then allocate and schedule the resources required to perform the needed maintenance at minimum cost.

Webinar: Mathematical Optimization + Machine Learning

Mathematical Optimization and Machine Learning (ML) are different but complementary technologies. Simply put – Mixed Integer Programming (MIP) answers questions that ML cannot. Machine learning makes predictions while MIP makes decisions. In this webinar, you will hear the results of the 2019 Mathematical Optimization Survey commissioned by Gurobi and conducted by Forrester and insights on how Data Scientists can use tools such as MIP to make complex decisions.

See how Blue Yonder uses machine learning and math programming to deliver daily, automated retail pricing decisions.

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