December 16, 2021 - Alex Strick van Linschoten - 1 min read
Last updated: February 16, 2022.
This week, we spoke with Danny Leybzon, currently working with WhyLabs to help data scientists monitor their models in production and prevent model performance from degrading. He previously worked as a kind of roving data scientist and engineer, helping companies put their models into production. We were lucky to get to speak to Danny in our new episode of Pipeline Conversations.
You can get a taste of the discussion with this clip, in which Danny explains why it’s a problem if you only think about starting to monitor your model after it’s already been deployed.
We had a really interesting discussion of some of the ways that tooling and the general context for data science sometimes lets practitioners down, and of course we also discussed why monitoring and logging is actually a kind of baseline practice that should be part of any and every data scientist’s toolkit. Luckily for us, Danny added in a bunch of examples from his wide experience doing all this in the real world. Check out out the full episode below or however you prefer to listen to podcasts!