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Thursday, October 1 • 4:50pm - 5:20pm
Building Complex Data Analytics Pipelines with Ray - Qingqing Mao, Dascena

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Building scalable data analytics pipelines is challenging, especially when different subtasks may have different computational requirements and interdependencies. It becomes more challenging when you need to serve enterprise customers who have strict data security and privacy policies and require on-premise deployment. The scaling requirement and computational capacity often vary widely from site to site.

We have been using Ray to build natural language processing pipelines and healthcare analysis pipelines. The highly efficient serialization using a shared-memory object store is a perfect fit for handling our data-intensive jobs. Ray helps us narrow the gap between data science and engineering, and it enables our data scientists to write high-performance and cost-efficient data analytics pipelines that can scale. 

avatar for Qingqing Mao

Qingqing Mao

Head of Engineering and Data Science, Dascena
Qingqing Mao is the Head of Engineering and Data Science at Dascena, where he leads the development of compliant and scalable clinical data pipelines and the research on applying machine learning techniques in healthcare and medicine. Previously, he worked as a senior staff data scientist... Read More →

Thursday October 1, 2020 4:50pm - 5:20pm PDT
Virtual 2