29 citations · 129 across the 23 of their papers we have counts for
6 papers · 1 filter
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…
Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows
Doris Jung-Lin Lee, Dixin Tang, Kunal Agarwal +8
Exploratory data science largely happens in computational notebooks with dataframe APIs, such as pandas, that support flexible means to transform, clean, and analyze data. Yet, vis…
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…
Deconstructing Categorization in Visualization Recommendation: A Taxonomy and Comparative Study
Doris Jung-Lin Lee, Vidya Setlur, Melanie Tory +2
Visualization recommendation (VisRec) systems provide users with suggestions for potentially interesting and useful next steps during exploratory data analysis. These recommendatio…
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…