Publications (18)
Vamsa: Automated Provenance Tracking in Data Science Scripts
Mohammad Hossein Namaki, Avrilia Floratou, Fotis Psallidas +5
There has recently been a lot of ongoing research in the areas of fairness, bias and explainability of machine learning (ML) models due to the self-evident or regulatory requiremen…
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…
PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers
Saeed Amizadeh, Sergiy Matusevych, Markus Weimer
There have been recent efforts for incorporating Graph Neural Network models for learning full-stack solvers for constraint satisfaction problems (CSP) and particularly Boolean sat…
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…
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…
Coded Elastic Computing
Yaoqing Yang, Matteo Interlandi, Pulkit Grover +3
Cloud providers have recently introduced new offerings whereby spare computing resources are accessible at discounts compared to on-demand computing. Exploiting such opportunity is…
Scaling Datalog for Machine Learning on Big Data
Yingyi Bu, Vinayak Borkar, Michael J. Carey +5
In this paper, we present the case for a declarative foundation for data-intensive machine learning systems. Instead of creating a new system for each specific flavor of machine le…
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
Ashvin Agrawal, Rony Chatterjee, Carlo Curino +19
Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios inc…
FLAML: A Fast and Lightweight AutoML Library
Chi Wang, Qingyun Wu, Markus Weimer +1
We study the problem of using low computational cost to automate the choices of learners and hyperparameters for an ad-hoc training dataset and error metric, by conducting trials o…
Iterative MapReduce for Large Scale Machine Learning
Joshua Rosen, Neoklis Polyzotis, Vinayak Borkar +5
Large datasets ("Big Data") are becoming ubiquitous because the potential value in deriving insights from data, across a wide range of business and scientific applications, is incr…
MLOS: An Infrastructure for Automated Software Performance Engineering
Carlo Curino, Neha Godwal, Brian Kroth +9
Developing modern systems software is a complex task that combines business logic programming and Software Performance Engineering (SPE). The later is an experimental and labor-int…
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…
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…
Batch-Expansion Training: An Efficient Optimization Framework
MichaÅ DereziÅski, Dhruv Mahajan, S. Sathiya Keerthi +2
We propose Batch-Expansion Training (BET), a framework for running a batch optimizer on a gradually expanding dataset. As opposed to stochastic approaches, batches do not need to b…
Towards Geo-Distributed Machine Learning
Ignacio Cano, Markus Weimer, Dhruv Mahajan +2
Latency to end-users and regulatory requirements push large companies to build data centers all around the world. The resulting data is "born" geographically distributed. On the ot…
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…
Large-Scale Automatic Audiobook Creation
Brendan Walsh, Mark Hamilton, Greg Newby +8
An audiobook can dramatically improve a work of literature's accessibility and improve reader engagement. However, audiobooks can take hundreds of hours of human effort to create,…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…