Machine learning (ML) brings exciting new opportunities, but applying the technology in production workloads has been cumbersome, time consuming, and error prone. Data generation patterns have evolved, generating streams of discrete events that require high-speed processing at 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 on November 12, learn how Hazelcast enables the application of ML algorithms on real-time data streams with a distributed, cooperative, low latency architecture. Additionally, we'll examine how Intel is expanding the possibilities for in-memory platform applications.
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