“The Gurobi Python modeling extensions greatly simplify the process of developing our optimization models. They work well as a part of our software stack.”
Andrew MartinezLead Analyst, AMS
As a data scientist, your curiosity, diligence, and creativity drive you to extract immense value from your data and models. But what if you could generate optimized decision recommendations, based on your predicted future—to directly influencing business decision-making? With Gurobi, you can.
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“We’re aiming to connect the world of data science with the world of optimization. With Gurobi, you can take your machine learning ‘black box’ that’s generating your predictions and plug it directly into your optimization model—enabling you to connect your forecasting with optimization.”
Dr. Tobias Achterberg, Vice President of Research and Development, Gurobi Optimization
With Gurobi Machine Learning—an open-source Python project to embed trained machine learning models directly into Gurobi—data scientists can more easily tap into the power of mathematical optimization.
Gurobipy Pandas is our convenient wrapper library to connect pandas with gurobipy. It enables users to efficiently build mathematical optimization models from data stored in DataFrames and Series and extract solutions as pandas objects.
Gurobi OptiMods is an open-source Python repository of implemented optimization use cases using Gurobi, each with clear and informative documentation that explains how to use it and the mathematical model behind it.
Prescriptive analytics tools like mathematical optimization help you make decisions based on your real-world business goals (“objectives”) and limitations (“constraints.”) This can be especially useful when you’re facing a business problem with multiple, conflicting goals (such as cutting spending while increasing production) and multiple constraints (such as time, distance, product availability).
Learn more about prescriptive analytics in our article, “What is Prescriptive Analytics?”
Predictive analytics seeks to identify patterns in data to forecast future events, such as predicting cyberattacks or imminent machine failures. Prescriptive analytics, on the other hand, utilizes mathematical modeling to guide decisions based on real-world objectives and constraints, such as minimizing costs or managing raw material inventory.
While predictive analytics tells you what might happen, prescriptive analytics provides actionable recommendations on how to achieve specific goals, given certain limitations.
Learn more about the difference in our article, “Predictive Analytics vs. Prescriptive Analytics.”
In the real world, prescriptive analytics has diverse applications, including transportation providers like Air France and Uber using it to create optimal routing, staffing, and maintenance plans. Professional sports leagues, such as the National Football League, plan their game schedules using prescriptive analytics. Additionally, manufacturers utilize prescriptive analytics to plan and manage the procurement, production, and distribution of their products, aligning decisions with real-world goals and constraints.
Learn more about examples in our article, “Examples of Prescriptive Analytics.”
Yes! By using machine learning predictions as valuable input for mathematical optimization solutions, or conversely, using mathematical optimization to inform machine learning predictions, you can leverage the problem-solving power of mathematical optimization to enhance machine-learning applications.
Learn more in our article, “Improving Machine Learning Applications with Prescriptive Analytics.”
Say you were planning a trip. Predictive analytics can predict what you may encounter along your journey (weather, traffic, engine trouble), and prescriptive analytics can, given those predictions, identify the route that best helps you achieve your goals (fastest, cheapest, safest route), given your constraints (time, budget, speed limits).
Here are some additional examples:
Learn more in our article, “How Can Prescriptive and Predictive Analytics Work Together?”
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