Applied Machine Learning in Real-Time with Distributed, Scalable, In-Memory Technology
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Machine learning (ML) brings exciting new opportunities, but applying the technology in production workloads has been cumbersome, time consuming, and error prone. In parallel, data generation patterns have evolved, generating streams of discrete events that require high-speed processing at extremely low response latencies. Enabling these capabilities requires a scalable application of high-performance stream processing, distributed application of ML technology, and dynamically scalable hardware resources.
In this webinar, learn how the Hazelcast In-Memory Computing Platform enables the application of ML (Java, Python, C++) algorithms on real-time data streams with a distributed, cooperative, low latency architecture. Additionally, we’ll examine how Intel’s new 2nd generation processors coupled with Intel Optane memory capabilities are expanding the possibilities for in-memory platform applications.
