What is the best proportion of different crops to grow together in the same field to maximize yield at harvest time? Do you get flats when you pump your bike tyres to a higher pressure than recommended? What is the perfect amount of time to boil an egg to get that center soft but the white cooked?
All these repeating problems can be framed as data optimization problems. In our lives and work, we make many repeated decisions, both big and small, which benefit from being optimised.
Optimize combines the latest research from the University of Oxford and advanced software engineering to deliver efficient data optimization to you via a robust and user-friendly API.
Optimize is a global data optimization tool. This means that it does not rely on any special properties of your problem to find the best configurations - it is a general purpose optimization solution.
To do this, Optimize implements Bayesian Optimization under the hood.
Bayes was an 18th century statistician. He invented a method of reasoning about risk which incorporates both evidence and prior knowledge which we call Bayesianism.
Bayesian Optimization leverages this method of reasoning to efficiently find optimal configurations for anything that requires parameterisation.
Is the process you want to optimize a real and messy procedure, like planting your crop for the season or manufacturing a new component to an industry standard? Or is your problem highly complex and sensitive to its parameter settings, like a complex data science pipeline?
Bayesian Optimization is particularly efficient when it is either very expensive for you to evaluate a single set of parameters, when the relationship of performance to the parameter settings is unknown or very complicated, or when the measurement of performance is a noisy process.