Learning is an analytic process of exploring the past in order to predict the future. Hence, being able to travel back in time to create features is critical for machine learning projects to be successful. To enable this, we built a time machine that computes features for any arbitrary time in the recent past for offline experimentation. We also built a real-time stream processing system to capture the interests of members during different times of the day and to quickly adapt to changes in the collective interests of members as it happens in case of real-world events.
Building the time machine for offline experimentation and the real-time infrastructure for online recommendations with Apache Spark (Streaming) and Apache Cassandra empowered us to both scale up the data size by an order of magnitude and train and validate the models in less time. We will delve into the architecture, use case details, data models used for cassandra and share our learnings.
About the Speakers
Prasanna Padmanabhan Engineering Manager, Netflix
Prasanna leads the Data Systems for Personalization team at Netflix. His primary focus is on building various big data infrastructure components that help their algorithmic engineers to innovate faster and improve personalization for Netflix members. In the past, he has built distributed data systems that leverages both batch and stream processing.
Roopa Tangirala Engineering Manager, Netflix
Roopa Tangirala is an experienced engineering leader with extensive background in databases, be they distributed or relational. She manages the database engineering team at Netflix responsible for operating cloud persistent and semipersistent runtime stores for Netflix, which includes Cassandra, Elasticsearch, Dynomite and MySQL databases, by ensuring data availability, durability, and scalability to meet the growing business needs.