Turning data into actionable insights – ZZ

This year, we are participating in the Lesbians Who Tech Pride Summit, June 12-16th. A Qualtrics booth will be set up for the summit and ZZ, a Senior Manager in Machine Learning here at Qualtrics, was chosen as a speaker for this event. Her presentation on: Compressing Cross-Lingual Multi-Task Models at Qualtrics is June 12 at 1:00pm PST for those who want to tune in! Here’s a bit about ZZ, what she does at Qualtrics, and how she got into the field of AI and Machine Learning.

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I am a Senior Manager at Qualtrics in the AI department. My team’s mission is to apply state-of-the-art AI/ML technologies to help Qualtrics customers to unleash the full power of the large amount of data they collected through Qualtrics™. We turn data into deep and actionable insights to help customers improve their business and organization through AI. In my spare time, I enjoy playing and learning together with my two kids and volunteering in all kinds of school activities to help more kids.   

Tell us a bit about what you’re presenting at this tech summit

Experience management is an emerging business area where organizations focus on understanding the feedback of customers and employees in order to improve their end-to-end experiences. This results in a unique set of machine learning problems to help understand how people feel, discover issues they care about, and find which actions need to be taken on data that are different in content and distribution from traditional NLP domains. In this presentation, we present a case study of building text analysis applications that perform multiple classification tasks efficiently in 12 languages in the nascent business area of experience management. In order to scale up modern ML methods on experience data, we leverage cross lingual and multi-task modeling techniques to consolidate our models into a single deployment to avoid overhead. We also make use of model compression and model distillation to reduce overall inference latency and hardware cost to the level acceptable for business needs while maintaining model prediction quality. 

How did you get into this field of AI Machine Learning? 

When I was studying computer science for undergraduate, I found the area I am most interested in is to analyze and find patterns from data. That’s why I decided to go to graduate school in the field of data mining and machine learning. After finishing my Phd in data mining, I worked in the AI industry for different roles till now. 

Any advice for women wanting to get into this field?

I have seen many women enjoying the field of AI and data science and are successful in this field. I would say if you are interested in this field, you would want to build a good knowledge on statistics and learn coding, especially coding related to data. One of the good  resources is Anaconda learning. Then, you can start to practice, practice and practice by doing projects (such as kaggle projects) you are interested in and build your expertise step by step.