Make Your Data Science Actionable: Real-Time Machine Learning Inference with Stream Processing


Are you ready to make your machine learning algorithms operational within your business in real time?

In this webinar, we will walk through an architecture for taking a machine learning model from training into deployment for inference within an Open Source platform for real-time stream processing.

We will also cover:

  • The typical workflow from data exploration to model training through to real-time model inference (aka scoring) on streaming data.
  • Important considerations to ensure maximum flexibility for deployments that need the flexibility to run in Cloud-Native, Microservices and Edge/Fog architectures.
  • A live demonstration of a working example of a machine learning model used on streaming data within Hazelcast Jet.

Presented By:

John DesJardins
John DesJardins
Field CTO and VP Solution Architecture

John DesJardins is currently Field CTO and VP Solution Architecture for North America at Hazelcast, where he is championing the growth of our Developer and Customer Community. His expertise in large scale computing spans Data Grids, Microservices, Cloud, Big Data, Internet of Things, and Machine Learning. He is an active blogger and speaker. John brings over 20 years of experience in architecting and implementing global scale computing solutions, including working with top Global 2000 companies while at Hazelcast, Cloudera, Software AG and webMethods. He holds a BS in Economics from George Mason University, where he first built predictive models, long before that was considered cool.