BrandPost: Deploying Deep Learning in Production Gains Multiple Efficiencies
TalkingData is a data intelligence service provider that offers data products and services to provide businesses insights on consumer behavior, preferences, and trends. One of TalkingData’s core services is leveraging machine learning and deep learning models to predict consumer behaviors (e.g., likelihood of a particular group to buy a house or a car) and use these insights for targeted advertising. For example, a car dealer will only want to show their ads to customers who the model predicts are most likely to buy a car in the next three months.
Initially, TalkingData was building an XGBoost model for these types of predictions, but their data science team wanted to explore whether deep learning models could have a significant performance improvement for their use case. After experimentation, their data scientists built a model on PyTorch, an open source deep learning framework, that achieved a 13% improvement on recall rate. (Recall rate is the percentage of times a model is able to give a prediction within a predefined confidence level.) In other words, their deep learning model managed to generate more predictions while maintaining a consistent level of accuracy.