Sep 23–26, 2019

Your cloud, your ML, but more and more scale? How SurveyMonkey did it

Jing Huang (SurveyMonkey), Jessica Mong (SurveyMonkey)
11:20am12:00pm Thursday, September 26, 2019
Location: 1A 23

Who is this presentation for?

Architects, Engineers and Data Scientists who are responsible for building and maintaining machine learning ecosystems within their organizations.




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, more than a year into the journey, we have seen a 10x increase in our model serving capability, all while building our ML platform on a hybrid cloud infra and expanding to multiple data centers.
To serve our fast-growing number of models operating in production, we extended our online ML serving layer to scale up our serving capability, added functionalities to support shadow testing, A/B testing, and different DS libraries. We built model continuous retraining pipeline to keep model effectiveness. We also developed a central feature store as the first step to democratizing ML. We have successfully solved complex engineering challenges on 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.

Prerequisite knowledge

Basic understanding of machine learning building blocks and development pipeline

What you'll learn

Learn how to expend ML capability with existing infra from SurveyMonkey’s journey. Learn what are the critical preparations before tackling the scalability issues in ML. Learn the key components of ML system. Learn the key considerations in terms of choosing third-party tools vs building in house solutions when you operates on a hybrid cloud. Learn the key building blocks to transform existing infrastructure to ML friendly.
Photo of Jing Huang

Jing Huang


Jing Huang is director of engineering, machine learning at SurveyMonkey, where she drives the vision and execution of democratizing machine learning. She is leading the effort to build the next-gen machine learning platform, oversees all machine learning operations. Previously she was an entrepreneur where she devoted her time to build mobile-first solutions and data products for non-tech industries. She also worked at Cisco Systems for six years, where her contribution range from security, cloud management to big data infrastructure.

Photo of Jessica Mong

Jessica Mong


Jessica Egoyibo Mong is an engineering manager on the Machine Learning Engineering (MLE) team at SurveyMonkey. She’s currently leading efforts to re-architect online serving ML system. Prior to the MLE team, she worked as a full-stack engineer on the Billing & Payments team, where she built and maintained software to enable SurveyMonkey’s global financial growth and operation. In past years, Jessica oversaw the technical talks program, jointly managed the engineering internship program, and co-led the Women in Engineering group. Jessica received a B.S in Computer Engineering from Claflin University in South Carolina. She is a 2014 White House Initiative on HBCUs All-Star, a Hackbright (Summer 2013) and CODE2040 (Summer 2014) alum. She has served on the leadership team of the Silicon Valley local chapter of the Anita Borg Institute and is a member of /dev/color. Jessica is a singer and upcoming drummer, and sings and drums at her church in Livermore, CA. In her spare time, she enjoys eating, CrossFit, reading, learning new technologies, and sleeping!

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