Mixed-Integer Programming The Most Powerful Tool to Hit Data Science Since Python

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Why should Mixed-Integer Programming (MIP) be a part of every company’s analytics toolbox?

“Nowadays, more and more companies are combining MIP and Machine Learning”. Gurobi CEO and Co-founder Ed Rothberg explains how MIP combines expressiveness and robustness to produce high-quality, reliable solutions.

 

Why Mixed-Integer Programming is 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. 

Watch this webinar to see real-world examples of Machine Learning and optimization in action, illustrating the value it can bring to your organization. It also provides you with the next steps on how to get started with optimization as well as available resources.

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.

Listen to this podcast to discover how machine learning and optimization can complement each other; the former making predictions about likely future business outcomes, and the latter suggesting appropriate actions to take in order to take advantage of these outcomes.

   

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