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VOOZH | about |
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VOOZH | about |
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The business case for real-time AI is clear. Personalization drives engagement, real-time inference drives revenue, and context-rich machine learning is becoming the baseline expectation for products. The hard part isn’t necessarily building the models; it’s the underlying infrastructure that does the heavy lifting of instantly knowing user history, preference, behaviour, and context.
But without a real-time data layer built for modern AI workloads, legacy architecture may be susceptible to unpredictable tail latency spikes, system failures during traffic surges, and platform teams operating in reaction mode instead of building. You may have been looking for intelligent, personalized, real-time experiences, but what you got was unpredictable performance, operational complexity, and an infrastructure bill that keeps increasing.
Your real-time data layer shouldn’t just be a supporting player in your AI stack; it is the foundation that everything else depends on. And when it’s done right, it should serve increasingly large contextual databases with predictable latency, scale efficiently without exponential cost, and give platform teams the operational stability to stay out of the critical path.
If your team is running into these challenges, join us at 10 a.m. Pacific/ 1 p.m. Eastern on Wednesday, March for “Scaling Real-Time AI & ML Workloads for Performance and Efficiency,” featuring DragonflyDB co-founder and CEO Oded Poncz.
In this technical session, Oded will join our host, Chris Pirillo, to break down why real-time context has become the core requirement for intelligent systems, where legacy architectures fall short, and what purpose-built infrastructure like Dragonfly actually makes possible. They’ll arm you with a clear framework for designing and operating a real-time data layer that can support modern AI and ML workloads without the operational compromise.
Register for this free webinar today. Can’t join live? Register anyway, and we’ll send you the recording.
