Aspect	Batch Learning	Windowed Inference
Primary Purpose	Train or retrain a model using a large dataset processed all at once.	Perform predictions on continuous or streaming data in segmented windows.
Data Type	Static, offline datasets.	Streaming or time-series data.
Processing Style	Processes the full dataset in a single batch or in large chunks.	Processes small, fixed-size windows (e.g., 1s, 5s, 30s) of the stream.
When It Runs	Periodically (e.g., nightly retraining).	Continuously in real time or near-real-time.
Typical Output	A new trained model.	A sequence of predictions per window (e.g., anomaly scores, event detections).
System Requirements	High compute and memory for large datasets.	Low latency processing as data arrives.
Common Use Cases	Model training, retraining, hyperparameter tuning.	Sensor monitoring, audio/video segmentation, fault detection.
Similarity 1	Both operate on subsets of data (batch vs window), rather than individual samples one-by-one.
Similarity 2	Both can apply the same model logic repeatedly over chunks of data.
Similarity 3	Both reduce overhead by grouping data instead of processing every sample independently.
Main Difference	Optimized for offline model training.	Optimized for real-time inference on streaming data.
Analogy	"Teaching the model new skills."	"Using the model repeatedly on moving slices of data."