43 citations · 288 across the 47 of their papers we have counts for
3 papers · 1 filter
Operationalizing Machine Learning: An Interview Study
Shreya Shankar, Rolando Garcia, Joseph M. Hellerstein +1
Organizations rely on machine learning engineers (MLEs) to operationalize ML, i.e., deploy and maintain ML pipelines in production. The process of operationalizing ML, or MLOps, co…
Towards Observability for Production Machine Learning Pipelines
Shreya Shankar, Aditya Parameswaran
Software organizations are increasingly incorporating machine learning (ML) into their product offerings, driving a need for new data management tools. Many of these tools facilita…
Fine-Grained Lineage for Safer Notebook Interactions
Stephen Macke, Hongpu Gong, Doris Jung-Lin Lee +3
Computational notebooks have emerged as the platform of choice for data science and analytical workflows, enabling rapid iteration and exploration. By keeping intermediate program…