This episode currently has no reviews.
Submit ReviewAbout This Episode
Shreya Shankar is a computer scientist, PhD student in databases at UC Berkeley, and co-author of "Operationalizing Machine Learning: An Interview Study", an ethnographic interview study with 18 machine learning engineers across a variety of industries on their experience deploying and maintaining ML pipelines in production.
Shreya explains the high-level findings of "Operationalizing Machine Learning"; variables that indicate a successful deployment (velocity, validation, and versioning), common pain points, and a grouping of the MLOps tool stack into four layers. Shreya and Lukas also discuss examples of data challenges in production, Jupyter Notebooks, and reproducibility.
Show notes (transcript and links): http://wandb.me/gd-shreya
---
๐ฌ *Host:* Lukas Biewald
---
*Subscribe and listen to Gradient Dissent today!*
๐ Apple Podcasts: http://wandb.me/apple-podcastsโโ
๐ Google Podcasts: http://wandb.me/google-podcastsโ
๐ Spotify: http://wandb.me/spotifyโ
This episode currently has no reviews.
Submit ReviewThis episode could use a review! Have anything to say about it? Share your thoughts using the button below.
Submit Review