Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA
dong meng

dong meng
Data Scientist, MapR


Dong Meng is a data scientist at MapR, where he helps customers solve their business problems with big data by translating the value from customers’ data and turns it into actionable insights or machine learning products. His recent work includes integrating open source machine learning frameworks like PredictionIO and XGBoost with MapR’s platform. He also created time series QSS and deep learning QSS as a MapR service offering. Dong has several years of experience in statistical machine learning, data mining, and big data product development. Previously, he was a senior data scientist with ADP, where he built machine learning pipelines and data products for HR using payroll data to power ADP Analytics, and a staff software engineer with IBM, SPSS, where he was part of the team that built Watson analytics. During his graduate study at the Ohio State University, Dong served as research assistant, where he concentrated on compressive sensing and solving point estimation problems from a Bayesian perspective.


11:50am12:30pm Thursday, March 8, 2018
dong meng (MapR)
Average rating: ***..
(3.33, 3 ratings)
Deep learning model performance relies on underlying data. Dong Meng offers an overview of a converged data platform that serves as a data infrastructure, providing a distributed filesystem, key-value storage and streams, and Kubernetes as orchestration layer to manage containers to train and deploy deep learning models using GPU clusters. Read more.