In this talk I will present a technique for deploying machine learning models to provide real-time predictions using Apache Pulsar Functions. In order to provide a prediction in real-time, the model usually receives a single data point from the caller, and is expected to provide an accurate prediction within a few milliseconds.

Throughout this talk, I will demonstrate the steps required to productionize a fully-trained ML that predicts the delivery time for a food delivery service based upon real-time traffic information, the customer;s location, and the restaurant that will be fulfilling the order.

Speakers:

David Kjerrumgaard: David is the author of “Pulsar in Action” and also a Principal Software Engineer on Splunk’s messaging team that is responsible for Splunk’s internal Pulsar-as-a-Service platform Prior to Splunk, he was the Director of Solution Architecture at Streamlio where he was responsible for the development of best practices and solutions based on Apache Pulsar.