1 code implementation • 13 Oct 2020 • Pulkit Sharma, Shezan Rohinton Mirzan, Apurva Bhandari, Anish Pimpley, Abhiram Eswaran, Soundar Srinivasan, Liqun Shao
Understanding predictions made by Machine Learning models is critical in many applications.
no code implementations • 23 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.
1 code implementation • 20 Oct 2018 • Mark Hamilton, Sudarshan Raghunathan, Ilya Matiach, Andrew Schonhoffer, Anand Raman, Eli Barzilay, Karthik Rajendran, Dalitso Banda, Casey Jisoo Hong, Manon Knoertzer, Ben Brodsky, Minsoo Thigpen, Janhavi Suresh Mahajan, Courtney Cochrane, Abhiram Eswaran, Ari Green
We introduce Microsoft Machine Learning for Apache Spark (MMLSpark), an ecosystem of enhancements that expand the Apache Spark distributed computing library to tackle problems in Deep Learning, Micro-Service Orchestration, Gradient Boosting, Model Interpretability, and other areas of modern computation.