Deploying Forecasts in AI & Artificial Intelligence

There are two main ways to deploy a forecast. Batch Inference is the most common; you run your model once a day or week on a schedule and store the results in a database. This is simple and cost-effective. Real-time Inference is needed if your predictions must change the moment a new data point arrives (e.g., high-frequency trading or dynamic pricing). Real-time is much more complex, as it requires a low-latency pipeline to feed the model its recent history.

In production, your model needs the Context of the past. If you have an AR(7) model, the API needs the last 7 days of data to predict tomorrow. This is where a Feature Store comes in. Instead of the API querying a slow analytics database, it pulls the 'Latest 7' from a high-speed cache like Redis. Ensuring that the data in this cache is identical to the data used during training is the key to preventing Train-Serve Skew.

Time-series models are particularly sensitive to Concept Drift. The world changes, and a model trained on 2023 patterns might fail in 2024. Your deployment must include an Automated Monitoring Loop. You compare your model's predictions to the actual values as they arrive. If the error (MAE/RMSE) exceeds a threshold, the system should trigger an alert or even start an Automated Retraining Pipeline with the latest data.