Digital Download
Why AI breaks at scale
AI systems fail when training and inference rely on different data, and when models operate on stale or incomplete context.
Production systems run on continuous events, not static datasets. Training pipelines rely on historical data, while inference depends on fresh context. When those two drift apart, model outputs degrade quickly.
Many teams try to patch this with batch pipelines and point-to-point integrations. Over time, that creates technical debt that slows down new AI development and makes systems harder to trust.
This infographic breaks down where these gaps come from and how streaming architectures help close them.
What you’ll learn‍
- The difference between training data and inference-time context
- Why stale or incomplete data leads topoor model performance
- The most common ways AI systemsdegrade in production
- How streaming enables continuous data, feedback, and evaluation
- The “agentic data plane” pattern andhow it supports flexible, real-time AI systems
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