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Data in engineering systems doesn't exist in isolation. It's invariably influenced by the surrounding context, be it other system variables, external factors, or broader operational settings.
Our model is equipped to ingest not just the raw data but also its associated context. Contextual embeddings, which are dense vector representations encapsulating this context, are fused with the primary data inputs. This ensures that the model's predictions are not just based on historical patterns but are also contextually aware.
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