Top Data Validation Tools for Machine Learning

Author(s): Eryk Lewinson

Originally published on Towards AI.

Discover Python tools that can catch any issues with your data!
Image generated with Midjourney

It was challenging to stop myself from starting this article with some variation of the popular phrase “garbage in, garbage out.” Well, I did it anyway. But jokes aside, we can easily imagine a situation in which we have built and deployed a machine learning model (possibly a black box) that accepts some input and returns some predictions. So far, so good.

However, with tons of complexity happening before the model (data preprocessing and manipulation), the model itself, and any post-processing of the outputs, many things can go wrong. And in some mission-critical fields (finance, healthcare, or security), there can be no margin of error, as crucial decisions are made based on the insights generated by ML models. In the unlikely scenario of unexpected events, having validation in the pipelines responsible for data handling and processing can be reassuring and enable troubleshooting problem areas.

That is why this article will discuss the importance of data validation. We will start by describing data validation in more detail. Then, we will cover the five most popular (Python) tools that we can use to validate our input/output data. We picked these tools due to their widespread adoption by the biggest companies (FAANG,… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI