Speaker "Jing Huang" Details Back



SurveyMonkey's Journey of building a scalable machine learning platform


As a leading global software company, SurveyMonkey created the online survey category and transformed the way people give feedback. The amount of people powered data (50+ billion questions answered on the platform, 2.4 million survey respondents per day, etc.) collected over the past two decades is a gold mine for ML. In early 2018, we started a journey with an objective to expand our machine learning capabilities and empower the rest of the company to leverage the power of ML. Now, 2 years into the journey, we extended our online ML serving layer, we built model continuous retraining pipelines, we developed a central feature store to improve the process of pushing a model to production. We have successfully solved complex engineering challenges along the way and have seen exciting results in return. While we are far from done, there are a lot of lessons worth sharing from our journey. We invite you to join us for a panel discussion with the core engineers on the team. The panelists will share their critical lessons learned from different perspectives.
Who is this presentation for?
Architects, Engineers and Data Scientists, ML leaders who are responsible for building and maintaining machine learning ecosystems within their organizations.
Prerequisite knowledge:
Good understanding of machine learning building blocks and development pipeline.
What you'll learn?
The key building blocks to transform existing infrastructure to support ML workflows. How to build a strong ML team from inside out. How to collaborate with x-function teams more effectively.


Jing Huang is a director of engineering, machine learning, at SurveyMonkey, where she drives the vision and execution of democratizing machine learning. She leads the effort to build the next-generation machine learning platform and oversees all machine learning operation projects. Previously, she was an entrepreneur and devoted her time to building mobile-first solutions and data products for nontech industries and worked at Cisco, where her contribution ranged from security and cloud management to big data infrastructure.