About 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 --- 💬 *Host:* Lukas Biewald --- *Subscribe and listen to Gradient Dissent today!* 👉 Apple Podcasts: http://wandb.me/apple-podcasts 👉 Google Podcasts: http://wandb.me/google-podcasts 👉 Spotify: http://wandb.me/spotify
