Search Results for author: Doris Xin

Found 8 papers, 0 papers with code

Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities

no code implementations30 Mar 2021 Doris Xin, Hui Miao, Aditya Parameswaran, Neoklis Polyzotis

Machine learning (ML) is now commonplace, powering data-driven applications in various organizations.

Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows

no code implementations13 Jan 2021 Doris Xin, Eva Yiwei Wu, Doris Jung-Lin Lee, Niloufar Salehi, Aditya Parameswaran

Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning.

AutoML

Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development

no code implementations4 May 2020 Angela Lee, Doris Xin, Doris Lee, Aditya Parameswaran

It is well-known that the process of developing machine learning (ML) workflows is a dark-art; even experts struggle to find an optimal workflow leading to a high accuracy model.

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.

Helix: Holistic Optimization for Accelerating Iterative Machine Learning

no code implementations14 Dec 2018 Doris Xin, Stephen Macke, Litian Ma, Jialin Liu, Shuchen Song, Aditya Parameswaran

Machine learning workflow development is a process of trial-and-error: developers iterate on workflows by testing out small modifications until the desired accuracy is achieved.

Helix: Accelerating Human-in-the-loop Machine Learning

no code implementations3 Aug 2018 Doris Xin, Litian Ma, Jialin Liu, Stephen Macke, Shuchen Song, Aditya Parameswaran

Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows -- by modifying the data pre-processing, model training, and post-processing steps -- via trial-and-error to achieve the desired model performance.

Structured Prediction

How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature

no code implementations27 Mar 2018 Doris Xin, Litian Ma, Shuchen Song, Aditya Parameswaran

A quantitative characterization of iteration can serve as a benchmark for machine learning workflow development in practice, and can aid the development of human-in-the-loop machine learning systems.

MLlib: Machine Learning in Apache Spark

no code implementations26 May 2015 Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar

Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks.

Cannot find the paper you are looking for? You can Submit a new open access paper.