Search Results for author: Carlo Curino

Found 8 papers, 2 papers with code

A Tensor Compiler for Unified Machine Learning Prediction Serving

1 code implementation9 Oct 2020 Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi

Machine Learning (ML) adoption in the enterprise requires simpler and more efficient software infrastructure---the bespoke solutions typical in large web companies are simply untenable.

MLOS: An Infrastructure for Automated Software Performance Engineering

1 code implementation1 Jun 2020 Carlo Curino, Neha Godwal, Brian Kroth, Sergiy Kuryata, Greg Lapinski, Si-Qi Liu, Slava Oks, Olga Poppe, Adam Smiechowski, Ed Thayer, Markus Weimer, Yiwen Zhu

In this paper we present: MLOS, an ML-powered infrastructure and methodology to democratize and automate Software Performance Engineering.

Data Science through the looking glass and what we found there

no code implementations19 Dec 2019 Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Matteo Interlandi, Avrilia Floratou, Konstantinos Karanasos, Wentao Wu, Ce Zhang, Subru Krishnan, Carlo Curino, Markus Weimer

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 an ever-growing community of data science (DS) practitioners.

Extending Relational Query Processing with ML Inference

no code implementations1 Nov 2019 Konstantinos Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Supun Nakandal, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, Carlo Curino

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 consequential steps of model storage and inference.

Griffon: Reasoning about Job Anomalies with Unlabeled Data in Cloud-based Platforms

no code implementations23 Aug 2019 Liqun Shao, Yiwen Zhu, Abhiram Eswaran, Kristin Lieber, Janhavi Mahajan, Minsoo Thigpen, Sudhir Darbha, SiQi Liu, Subru Krishnan, Soundar Srinivasan, Carlo Curino, Konstantinos Karanasos

In contrast, in Griffin we cast the problem to a corresponding regression one that predicts the runtime of a job, and show how the relative contributions of the features used to train our interpretable model can be exploited to rank the potential causes of job slowdowns.

Time Series Time Series Analysis

Towards Geo-Distributed Machine Learning

no code implementations30 Mar 2016 Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino, Giovanni Matteo Fumarola

Current solutions to learning from geo-distributed data sources revolve around the idea of first centralizing the data in one data center, and then training locally.

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