The document discusses Machine Learning at SurveyMonkey. It outlines current ML models like survey scoring, spam detection, and churn prediction. It then describes the ML platform and infrastructure using Python, Docker, and tools like Jupyter Notebook, SKLearn and TensorFlow. Future work includes improving the data pipeline and retraining scheduling. The document concludes by mentioning open ML engineer roles in Ottawa.

1. Machine Learning in
SurveyMonkey
Da Kuang
Machine Learning Engineer

2. 2
Agenda
•Our use cases (models) using machine learning
•The machine learning infrastructure and platform in SurveyMonkey
•Future work

3. 3
Survey Score Model

4. 4
Spammer Detection Model

5. 5
Churn Prediction Model

6. 6
ML Platform
Push the project
Build Package
Workspace GitHub
Scoring Nodes
Test locally
Push Package
Deploy Model
Package Server
Dataset Server

7. 7
Sample Data Science Project

8. 8
Our ML Engineering Stack
• Python
• Pyramid
• Gunicorn
• Nginx
• Ansible
• Docker

9. 9
Our Data Science Toolset
• Jupyter Notebook
• SKLearn
• Pandas
• Numpy
• Matplotlib
• Gensim
• FastText
• Spacy
• Tensorflow (TFLearn)
• XGBoost

10. 10
Future work
• Design DS project workflow to avoid training and prediction on different data source
• Data pipeline integration (Kafka, Cassandra, Spark Stream)
• Retraining Scheduling Engine

11. 11
We are hiring machine learning engineer in Ottawa.