MLOps Anti-Patterns

The Data Exchange Podcast: Nikhil Muralidhar on lessons learned from developing and deploying machine learning models at scale.

SubscribeApple • Android • Spotify • Stitcher • Google • AntennaPod • RSS.

This week’s guest is Nikhil Muralidhar,  a Graduate Research Assistant at Virginia Tech College of Engineering. He is the lead author of an excellent survey paper entitled “Using AntiPatterns to avoid MLOps Mistakes”.  Nikhil and his co-authors provide a vocabulary of anti-patterns encountered in ML pipelines, with a focus on the financial services industry. In addition, they make several recommendations for documenting and managing MLOps at an enterprise scale. More specifically, they describe the crucial role played by model certification authorities within enterprises.

Download the 2022 Data Engineering Survey Report and learn how companies are designing and building data and AI platforms.

Nikhil Muralidhar:

We decided on three different subgroups of anti-patterns. The first set of anti-patterns has to do with model design and development. The second set of anti-patterns actually have to do with performance evaluation. Various problems or various inconsistencies that actually arise if the model performance is not evaluated correctly. And the third is, you know, what traditional MLOps people might actually call deployment and maintenance.

… I would basically argue that this paper is sort of a celebration that machine learning has actually become so mature in the industry, that the reason we’re discussing all these things is because data driven discovery and data driven modeling has actually come to affect all of our lives in such a huge way.

Highlights in the video version:

Related content:

Subscribe to our Newsletter:
We also publish a popular newsletter where we share highlights from recent episodes, trends in AI / machine learning / data, and a collection of recommendations.

[Image: Sid Mosdell, Nature Pattern; usage via CC BY 2.0.]