Real-Time Data for Real-Time AI: Designing Event-Driven Cloud Architectures for Autonomous Decision
A few months ago I watched a fraud model flag a transaction correctly. The only problem was that it flagged it four minutes after the money had already left the account. The model was fine. The math was fine. The data pipeline was the thing that failed, quietly, the way pipelines usually do.
That incident stuck with me because it captures something a lot of AI teams still get wrong. We spend enormous energy tuning models, and comparatively little energy asking whether the data even arrives in time for the model's answer to matter. An "intelligent" system fed on stale data isn't intelligent. It's just slow and confident, which might be worse.
What "real-time" actually means here
I want to push back gently on how loosely the phrase "real-time" gets thrown around in vendor decks. Real-time isn't a fixed number. It's whatever window the decision needs. A recommendation engine might...
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