Make your Data Science Actionable, Real-Time Machine Learning Inference with Stream Processing – John DesJardins
We caught up with our upcoming speaker, John DesJardins, CTO and VP Solution Architecture at Hazelcast North America, ahead of our meetup on Thursday 7th March.
John’s expertise in large scale computing spans Microservices, Big Data, Internet of Things, Machine Learning and Cloud. He is an active blogger and speaker. John brings over 25 years of experience in architecting and implementing global scale computing solutions 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.
He’ll be joining us at David Game College, Aldgate to give his presentation on how to make your Data Science actionable: Real-Time Machine Learning inference with Stream Processing. We interviewed him ahead of the talk to find out what we can expect from the event and hear his advice for junior developers.
Who do you think should come along and why?
Java developers who would like to learn about basic concepts in stream processing and machine learning and how these can be integrated into Java Applications, as well as what are common use-cases for real-time machine learning.
What do you think are the three most interesting questions that this event will answer?
What is a workflow and architecture for moving predictive models from training by data science teams into real-time execution within Java applications?
What are some approaches to run models into a distributed architecture and how do these support patterns like micro services, Cloud-Native, Fog & Edge?
How do I ensure reliable execution as well as achieve scalability and availability when serving machine learning models in real-time?
Why do you think this presentation is important for people?
Smart Applications are becoming ever more embedded into our daily lives across devices. Therefore, developers need to understand how they can work with data science teams to integrate machine learning capabilities into their applications.
Any advice for junior developers entering the industry?
Learn more about machine learning and data science, as well as about newer data storage including NoSQL and data grids, and distributed compute technologies such as Spark and Jet.
If this sounds like your kind of event then check our event page for all the information and to reserve your spot.