29 citations · 110 across the 17 of their papers we have counts for
25 papers
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…
Rethinking Streaming Machine Learning Evaluation
Shreya Shankar, Bernease Herman, Aditya G. Parameswaran
While most work on evaluating machine learning (ML) models focuses on computing accuracy on batches of data, tracking accuracy alone in a streaming setting (i.e., unbounded, timest…
The Sky Above The Clouds
Sarah Chasins, Alvin Cheung, Natacha Crooks +14
Technology ecosystems often undergo significant transformations as they mature. For example, telephony, the Internet, and PCs all started with a single provider, but in the United…
Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities
Doris Xin, Hui Miao, Aditya Parameswaran +1
Machine learning (ML) is now commonplace, powering data-driven applications in various organizations. Unlike the traditional perception of ML in research, ML production pipelines a…
Enhancing the Interactivity of Dataframe Queries by Leveraging Think Time
Doris Xin, Devin Petersohn, Dixin Tang +5
We propose opportunistic evaluation, a framework for accelerating interactions with dataframes. Interactive latency is critical for iterative, human-in-the-loop dataframe workloads…
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows
Doris Xin, Eva Yiwei Wu, Doris Jung-Lin Lee +2
Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning. T…