961 citations · 1k across the 7 of their papers we have counts for
6 papers · 1 filter
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…
Towards Scalable Dataframe Systems
Devin Petersohn, Stephen Macke, Doris Xin +7
Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the remarkable success of dataframe libraries in Rand Python, dataframes face performance issu…
Extending Relational Query Processing with ML Inference
Konstantinos Karanasos, Matteo Interlandi, Doris Xin +10
The broadening adoption of machine learning in the enterprise is increasing the pressure for strict governance and cost-effective performance, in particular for the common and cons…
Helix: Holistic Optimization for Accelerating Iterative Machine Learning
Doris Xin, Stephen Macke, Litian Ma +3
Machine learning workflow development is a process of trial-and-error: developers iterate on workflows by testing out small modifications until the desired accuracy is achieved. Un…
Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities
Doris Xin, Litian Ma, Jialin Liu +3
Development of machine learning (ML) workflows is a tedious process of iterative experimentation: developers repeatedly make changes to workflows until the desired accuracy is atta…