Big Data and AI have unleashed possibility that was once a chapter in fictional stories. As these major tech revolutions take root in reality, we're faced with an interesting challenge to find convergence between the two. In this talk, I introduce a high level architecture of how enterprises can make AI/Big Data a reality for their organizations.
Open-source technologies allow developers to build microservices framework to build myriad real-time applications. One such application is building the real-time model scoring. In this session, we will showcase how to architect a microservice framework, in particular how to use it to build a low-latency, real-time model scoring system. At the core of the architecture lies Apache Spark's Structured Streaming capability to deliver low-latency predictions coupled with Docker and Flask as additional open source tools for model service. In this session, you will walk away with:
Knowledge of enterprise-grade model as a service Streaming architecture design principles enabling real-time machine learning Key concepts and building blocks for real-time model scoring Real-time and production use cases across industries, such as IIOT, predictive maintenance, fraud detection, sepsis etc.