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08/25/2023
ML-Powered Index Tuning: An Overview of Recent Progress and Open Challenges
The scale and complexity of workloads in modern cloud services have brou...
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06/12/2022
Stochastic Gradient Descent without Full Data Shuffle
Stochastic gradient descent (SGD) is the cornerstone of modern machine l...
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05/17/2021
Towards Demystifying Serverless Machine Learning Training
The appeal of serverless (FaaS) has triggered a growing interest on how ...
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02/15/2021
A Data Quality-Driven View of MLOps
Developing machine learning models can be seen as a process similar to t...
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10/16/2020
On Automatic Feasibility Study for Machine Learning Application Development with ease.ml/snoopy
In our experience working with domain experts who are using today's Auto...
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08/27/2020
Cost-based Query Rewriting Techniques for Optimizing Aggregates Over Correlated Windows
Window aggregates are ubiquitous in stream processing. In Azure Stream A...
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05/11/2020
Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions
Machine learning (ML) applications have been thriving recently, largely ...
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03/09/2020
A Note On Operator-Level Query Execution Cost Modeling
External query execution cost modeling using query execution feedback ha...
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12/19/2019
Data Science through the looking glass and what we found there
The recent success of machine learning (ML) has led to an explosive grow...
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06/01/2019
Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter
Simplifying machine learning (ML) application development, including dis...
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06/01/2019
Ease.ml/meter: Quantitative Overfitting Management for Human-in-the-loop ML Application Development
Simplifying machine learning (ML) application development, including dis...
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03/01/2019
Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment
Continuous integration is an indispensable step of modern software engin...
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08/24/2017
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads
We present ease.ml, a declarative machine learning service platform we b...
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07/29/2017