Online Inference with User Personalization at Scale
Powered by Upsolver
Applying machine learning models that rely on both historical user data and real-time data is often seen as an insurmountable engineering challenge. The need to build and orchestrate two complex architectures for offline model training and online inference creates data engineering overhead that leaves many real-time machine learning projects dead in the water.
In this webinar, Upsolver CTO Yoni Iny will present a data architecture, powered by Upsolver on AWS, for combining streaming and historical big data to apply a unified prediction model - and how you can build and launch this end-to-end data flow using a single architecture in less than an hour.
What you'll learn:
- The engineering challenge of applying online inference based on live data
- Reference architectures for real-time machine learning.
- How to dramatically simplify data science projects and architectures.
- Upsolver vs alternative architectures such as Spark Streaming