In order for businesses to thrive in the evolving AI landscape, it is essential to steer clear of the obstacles posed by outdated data-at-rest architectures. Instead, they should construct their contemporary AI framework on a basis of dynamic, real-time data. Experiment, scale, and innovate faster!
Achieving real-time AI demands more than fast algorithms; it requires trustworthy, relevant data served in the moment for smarter, faster insights. This is precisely why data streaming is emerging as the fundamental infrastructure for the modern AI stack.
You can’t train or infer on a proper AI model without real-time, high-quality, trusted data that’s delivered across all the hundreds of systems, apps, databases, etc. in the enterprise. Organisations need to ensure that their AI reasoning engine is being fed the right data, from the right source, no matter where it lives.
Continuously enriched, trustworthy data streams are key to building next-gen AI applications that are accurate and have the rich, real-time context modern use cases demand, enabling companies to build powerful AI applications.