This short video explains why companies use Hazelcast for business-critical applications based on ultra-fast in-memory and/or stream processing technologies.
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing?
In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.
Now, deploying Hazelcast-powered applications in a cloud-native way becomes even easier with the introduction of Hazelcast Cloud Enterprise, a fully-managed service built on the Enterprise edition of Hazelcast IMDG. Can't attend the live times? You should still register! We'll be sending out the recording after the webinar to all registrants.
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.
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.