Data observability is one of the hottest sectors of the market, and numerous startups have attracted big investments in their offerings. But when it comes to the impact of data on AI, data observability only gets you so far. Now a startup called WhyLabs is developing an AI observability platform that keeps an eye on the state of data as well as machine learning models.
WhyLabs was founded to combine elements of data observability with machine learning operations (MLOps) into a single platform that minimizes the work required in keeping AI applications running smoothly, according to Andy Dang, the head of engineering and a co-founder of Whylabs.
“The goal of the company is to first build a data observability platform for data and machine learning,” he says. “The second part we want to solve is end-to-end monitoring with regard to data health and model health when it comes to real-world operations.”
As data pipelines get bigger, companies find they need to devote more of their data engineer’s time to manually monitor them, manage them, and fix the little issues that life throws at you in the world of big data. The company estimates data engineers spend 40% of their time in these tasks, if required to do them manually.
Engineers need to be on the lookout for things that can cause a hiccup in the machine learning system. For example, a seemingly innocuous change to a minor piece of business logic can have a fairly big impact down the line.
One WhyLabs customer had their ML system rendered inoperable because an upstream data supplier had switched from a five-digit Zip Code to a nine-digit Zip code. COVID also threw many ML systems for a loop, although much of that was due to fundamental changes in human behavior and not some data or coding error. No matter how they arise, these can wreak havoc on applications that use ML.