51 citations · 144 across the 13 of their papers we have counts for
14 papers
What can Data-Centric AI Learn from Data and ML Engineering?
Neoklis Polyzotis, Matei Zaharia
Data-centric AI is a new and exciting research topic in the AI community, but many organizations already build and maintain various "data-centric" applications whose goal is to pro…
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…
Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)
Konstantinos, Katsiapis, Abhijit Karmarkar +17
Software Engineering, as a discipline, has matured over the past 5+ decades. The modern world heavily depends on it, so the increased maturity of Software Engineering was an eventu…
Improving Differentially Private Models with Active Learning
Zhengli Zhao, Nicolas Papernot, Sameer Singh +2
Broad adoption of machine learning techniques has increased privacy concerns for models trained on sensitive data such as medical records. Existing techniques for training differen…
Automated Data Slicing for Model Validation:A Big data - AI Integration Approach
Yeounoh Chung, Tim Kraska, Neoklis Polyzotis +2
As machine learning systems become democratized, it becomes increasingly important to help users easily debug their models. However, current data tools are still primitive when it…
The Case for Learned Index Structures
Tim Kraska, Alex Beutel, Ed H. Chi +2
Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a re…