LightAutoML: AutoML Solution for a Large Financial Services Ecosystem

Although AutoML rose to popularity a few years ago, the ealy work on AutoML dates back to the early 90’s when scientists published the first papers on hyperparameter optimization. It was in 2014 when ICML organized the first AutoML workshop that AutoML gained the attention of ML developers. One of the major focuses over the years of AutoML is the hyperparameter search problem, where the model implements an array of optimization methods to determine the best performing hyperparameters in a large hyperparameter space for a particular machine learning model. Another method commonly implemented by AutoML models is to estimate the probability of a particular hyperparameter being the optimal hyperparameter for a given machine learning model. The model achieves this by implementing Bayesian methods that traditionally use historical data from previously estimated models, and other datasets. In addition to hyperparameter optimization, other methods try to select the best models from a space of modeling alternatives. 

In this article, we will cover LightAutoML, an AutoML system developed primarily for a European company operating in the finance sector along with its ecosystem. The LightAutoML framework is deployed across various applications, and the results demonstrated superior performance, comparable to the level of data scientists, even while building high-quality machine learning models. The LightAutoML framework attempts to make the following contributions. First, the LightAutoML framework was developed primarily for the ecosystem of a large European financial and banking institution. Owing to its framework and architecture, the LightAutoML framework is able to outperform state of the art AutoML frameworks across several open benchmarks as well as ecosystem applications. The performance of the LightAutoML framework is also compared against models that are tuned manually by data scientists, and the results indicated stronger performance by the LightAutoML framework. 

This article aims to cover the LightAutoML framework in depth, and we explore the mechanism, the methodology, the architecture of the framework along with its comparison with state of the art frameworks. So let’s get started. 

Although researchers first started working on AutoML in the mid and early 90’s, AutoML attracted a major chunk of the attention over the last few years, with some of the prominent industrial solutions implementing automatically build Machine Learning models are Amazon’s AutoGluon, DarwinAI, H20.ai, IBM Watson AI, Microsoft AzureML, and a lot more. A majority of these frameworks implement a general purpose AutoML solution that develops ML-based models automatically across different classes of applications across financial services, healthcare, education, and more. The key assumption behind this horizontal generic approach is that the process of developing automatic models remains identical across all applications. However, the LightAutoML framework…