The KXY platform aims to Democratize Lean AI.

• - Derisk Your Machine Learning Projects: The KXY platform empowers you to never again start a machine learning project that will come to a dead-end.
  
• - Your Machine Learning Projects, 10x Cheaper & 10x Faster: The KXY platform empowers you to quantify the feasibility of your machine learning experiments (i.e. model and/or variable choice), so that you may focus on the high ROI ones. We estimate that 1 in 10 experiments are dead-ends. The KXY platform can therefore allow you to slash your compute cost and project duration tenfold.
  
• - Leverage Your Data, To Its Full Potential: Don't be satisfied with the performance you get from popular machine learning models. Determine how suboptimal your production model is, and get actionable insights on how to improve it.

What makes the KXY platform work is the realization that it is possible to estimate the feasibility of a machine learning experiment without actually runnning it. A machine learning experiment is considered feasible when it can yield a performance greater than a given baseline.

As it turns out, for a variety of metrics including R-squared, Root Mean Square Error, classification accuracy, to name but a few, it is possible to estimate the highest performance achievable when using a specific set of explanatory variables to predict a business outcome, without actually training any predictive model. We wrote the first paper explaining how to do this.

A useful analogy is oil production. Running a machine learning experiment without any informed expectation of its feasibility is like drilling an oil well to extract oil without any reason to believe that the site where the well is being drilled is rich in oil. Most of the time the endeavor will fail, thereby resulting in avoidable wastes of resources.

The oil and gas industry knows it very well and has created an entire field of study, namely exploration geophysics, dedicated to estimating the amount of oil in the ground and the expected ROI of an extraction project prior to, and independently from, launching the oil extraction project. Our machine learning papers lay out how to do this for machine learning experiments and projects.