The WhyLogs Blog

Machine learning (ML) data is big and messy. Organizations have increasingly adopted RAPIDS and cuML to help their teams run experiments faster and achieve better model performance on larger datasets.

It's hard to overstate the importance of monitoring data quality in ML pipelines. In this post we will explore an elegant solution with whylogs and MLflow, which allows for a more informed analysis of model performance.

by Isaac Backus, PhD Applied Data Scientist at WhyLabs In traditional software, logging and instrumentation have been adopted as standard practice to create transparency and to make sense of the health of a complex system. When it comes to AI applications, the lack of tools and standardized approaches mean that logging is often spotty and incomplete. Here, I compare two approaches to data logging: sampling and profiling. I have two goals in this post. First, I will demonstrate that profiling i

As companies across industries adopt AI applications in order to improve products and stay competitive, very few have seen a return on their investments. That’s because AI operations are expensive...

Pandora’s box, according to Greek mythology, was a consequence of mankind’s first technological revolution. It was because humans harnessed the power of fire that the gods sent them a package of misfortunes. The moral of the story seems to be that together with the advantages created by every technological leap forward, there also come unexpected difficulties. Over the past decade, my co-founders and I have experienced first hand the tremendous advantages as well as the difficulties of AI adop

Fire up your MLOps with a scalable, lightweight, open source data logging library Co-author: Bernease Herman We are thrilled to announce the open-source package whylogs. It enables data logging for any ML/AI pipeline in a few lines of code. Data logging is a critic...