Taking The Pain Out of Data Science- RecSys ML framework over Spark
Details
As part of ILTechTalks week 2018, we invite you to the following talk:
Taking The Pain Out of Data Science- RecSys ML framework over Spark
Outbrain is the world’s largest discovery platform, bringing personalized and relevant content to audiences while helping publishers understand their audiences through data.
Its recommender system is serving billions of content recommendations daily, based on millions of hourly user interactions.
Our predictive models span over a variety of supervised learning techniques, ranging from content-based recommenders, through behavioral models and all the way to collaborative techniques such as factorization machines. Agility and stability are crucial aspects of the system.
This talk will cover our journey towards solutions that would not compromise neither on scale nor on model complexity, and design a dynamic framework that shortens the cycle between research and production.
We will cover the different stages of the framework, including important take away lessons for data scientists as well as software engineers.
About Shaked:
Shaked is an Algorithm Engineer and Tech Lead at the Personalization team @ Outbrain's Recommendations group, developing large-scale machine learning algorithms for Outbrain's content recommendations platform, from data science to production.