53 citations · 296 across the 19 of their papers we have counts for
6 papers · 1 filter
Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines
Bojan Karlaš, David Dao, Matteo Interlandi +4
Developing modern machine learning (ML) applications is data-centric, of which one fundamental challenge is to understand the influence of data quality to ML training -- "Which tra…
A Tensor Compiler for Unified Machine Learning Prediction Serving
Supun Nakandala, Karla Saur, Gyeong-In Yu +4
Machine Learning (ML) adoption in the enterprise requires simpler and more efficient software infrastructure---the bespoke solutions typical in large web companies are simply unten…
Data Science through the looking glass and what we found there
Fotis Psallidas, Yiwen Zhu, Bojan Karlas +8
The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by…
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim +4
Classical Machine Learning (ML) pipelines often comprise of multiple ML models where models, within a pipeline, are trained in isolation. Conversely, when training neural network m…
Machine Learning at Microsoft with ML .NET
Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko +31
Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate t…
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Yunseong Lee, Alberto Scolari, Byung-Gon Chun +3
Machine Learning models are often composed of pipelines of transformations. While this design allows to efficiently execute single model components at training time, prediction ser…