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Operationalizing Machine Learning with Java Microservices and Stream Processing


Are you ready to take your algorithms to the next steps and get them working on real-world data in real-time? We will walk through an architecture for taking a machine learning model into deployment for inference within an open source platform designed for extremely high throughput and low latency.

We’ll demonstrate a working example of a machine learning model being used on streaming data within the Hazelcast In-Memory Computing Platform, a powerful technology for distributed in-memory processing. We will also touch on important considerations to ensure maximum flexibility for deployments that need the flexibility to run either on-premises or in the cloud.

Presented By:

Scott McMahon
Scott McMahon
Technical Director & Team Lead, Americas

Scott McMahon is the Technical Director & Team Lead, America st at Hazelcast® with over 20 years of software development and enterprise consulting experience. Before specializing in Hazelcast In-Memory Data Grid technology he built big data analytics platforms and business process management systems for many of the world’s leading corporations. He currently lives in Portland, Oregon, and when not working on computer systems, he enjoys getting outdoors and having fun with his family.