From Detection to Correction: How to Keep Your Production Data Clean and Reliable

Author(s): Youssef Hosni

Originally published on Towards AI.

Table of Contents:

In Production ML, data quality is everything. No matter how great your models or algorithms are, if the data you feed them is garbage, you’ll get garbage results. But how can you tell if your data is good or bad? That’s what we’re going to explore in this article.

We’ll start by discussing the importance of validating data and detecting data issues in production. Specifically, we’ll focus on two types of data issues: data and concept drift and schema and distribution skew. These issues can be difficult to detect, but they can have a significant impact on the accuracy and reliability… 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