In the realm of machine learning where data reigns supreme, maintaining effective model development and testing requires navigating the balance between data access and security restrictions. Recognizing this, Capital One steps up to the plate bringing a pioneering open-source project to light, dubbed as Synthetic Data.
Envisioned by Taylor Turner, Capital One's lead machine learning engineer, and co-contributor, Synthetic Data offers a novel solution to the age-old problem of safe data sharing and processing. The tool produces artificial data, dismissing the need for 'real' or personally identifiable data, thereby accelerating the idea generation and hypothesis testing processes.
While representative of the original data in its schema and statistical properties, Synthetic Data guarantees privacy, making it particularly beneficial where intricate, nonlinear datasets are required, as with deep learning models.
As explained by Brian Barr, a senior machine learning engineer, and researcher at Capital One, Synthetic Data operates by taking in statistical properties given by the model, i.e., inputs' marginal distribution, inputs' correlation, and an analytical expression mapping inputs to outputs, subsequently generating the desired dataset.
The creative freedom this framework offers is impressive, balancing simplicity and artistic malleability, making it a game-changer in machine learning, opined Barr.
But this is not the first time the notion of synthetic data has been broached. As Barr pointed out, previous attempts in the 80s have led to functionalities within the favored Python machine learning library, scikit-learn. However, as deep learning with nonlinear relationships came to the forefront, these functions were found to be restrictive and inadequate.
This trailblazing project sprouted from the fertile landing grounds of Capital One's machine learning research program. It seeks to elevate the methods, applications, and techniques of machine learning, tailoring banking to be more accessible and secure. Barr's investigative paper titled 'Towards Ground Truth Explainability on Tabular Data' served as the creative nucleus for Synthetic Data.
Moreover, Synthetic Data proves compatible with Data Profiler, Capital One's open-source machine learning library for large data monitoring and sensitive information detection. Data Profiler provides the statistics to represent the dataset, forming the basis of synthetic data creation.
As part of our commitment to driving research and advancing open-source tools, we are excited to delve deeper into the intersections between data profiling and synthetic data sharing those insights with the community, Turner stated.
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