ADA Lab @ UCSD

Project Triptych

Overview

Triptych is an end-to-end model selection management system (MSMS) that aims to simplify and accelerate the process of sourcing data/features and selecting ML models. Our guiding principles are to exploit the semantics of the data and the ML task to the extent possible to reduce work for the data scientist and reduce runtimes and costs. We apply these principles to remove or mitigate different bottlenecks in this end-to-end process, eventually unifying these components to yield an integrated ‘‘operating system’’ for ML analytics tasks. Please refer to the ACM SIGMOD Record paper below for more details of this vision.

Component Project Webpages

Hamlet Exploiting database schema information to simplify data sourcing.

Morpheus Integrating linear algebra and relational algebra to simplify feature engineering for ML.

Nimbus Enabling the first ML-aware cloud-based commodity market for the new black gold: training data.

SLAB The first comprehensive benchmark comparison of scalable linear algebra systems.

SortingHat ML schema inference and auto data prep.

Cerebro Efficient and Reproducible Model Selection on Deep Learning Systems..

Publications

• Enabling and Optimizing Non-linear Feature Interactions in Linear Algebra Over Normalized Data
  Side Li, Lingjiao Chen, and Arun Kumar
  ACM SIGMOD 2019 | Paper PDF | Code and Data coming soon

• Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent
  Fengan Li, Lingjiao Chen, Yijing Zeng, Arun Kumar, Jeffrey Naughton, Jignesh Patel, and Xi Wu
  ACM SIGMOD 2019 | Paper PDF | TechReport | Code and Data coming soon

• Model-based Pricing for Machine Learning in a Data Marketplace
  Lingjiao Chen, Paraschos Koutris, and Arun Kumar
  ACM SIGMOD 2019 | Paper PDF | TechReport | Code and Data coming soon

• Demonstration of Nimbus: Model-based Pricing for Machine Learning in a Data Marketplace
  Lingjiao Chen, Hongyi Wang, Leshang Chen, Paraschos Koutris, and Arun Kumar
  ACM SIGMOD 2019 (Demo) | Paper PDF | Video coming soon

• A Comparative Evaluation of Systems for Scalable Linear Algebra-based Analytics
  Anthony Thomas and Arun Kumar
  VLDB 2018/2019 | Paper PDF | TechReport | Code and Data

• Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers?
  Vraj Shah, Arun Kumar, and Xiaojin Zhu.
  VLDB 2018 | Paper PDF | TechReport | Code and Data

• Towards Linear Algebra over Normalized Data
  Lingjiao Chen, Arun Kumar, Jeffrey Naughton, and Jignesh Patel
  VLDB 2017 | Paper PDF | TechReport | Code and Data

• Model-based Pricing: Do Not Pay for More than What You Learn!
  Lingjiao Chen, Paraschos Koutris, and Arun Kumar
  ACM SIGMOD 2017 DEEM Workshop | Paper PDF

• Cerebro: A System to Manage Deep Learning for Relational Data Analytics
  Arun Kumar
  CIDR 2017 (Abstract) | Paper PDF

• To Join or Not to Join? Thinking Twice about Joins before Feature Selection
  Arun Kumar, Jeffrey Naughton, Jignesh M. Patel, and Xiaojin Zhu
  ACM SIGMOD 2016 | Paper PDF | TechReport | Code and Data

• Model Selection Management Systems: The Next Frontier of Advanced Analytics
  Arun Kumar, Robert McCann, Jeffrey Naughton, and Jignesh M. Patel
  ACM SIGMOD Record Dec 2015 (Vision Track) | Paper PDF

• Cerebro: Efficient and Reproducible Model Selection on Deep Learning Systems
  Supun Nakandala, Yuhao Zhang, and Arun Kumar
  Under Submission | TechReport

• The ML Data Prep Zoo: Towards Semi-Automatic Data Preparation for ML
  Vraj Shah, and Arun Kumar
  Under Submission | TechReport | Data Prep Zoo Repository on GitHub