EDGE OSDEFINITION
Batch inference refers to using a trained AI/Machine Learning model to make predictions at scale. This can be used when instant results are not needed and allows large sets of data to be processed efficiently. Batch inference can offer benefits to reduce overhead by allowing multiple predictions to be made.
Importance of Batch Inference
- It allows for large volumes of data to be processed using optimised computing resources.
- Large numbers of predictions (thousands or millions) can be made.
- Helps in better processing of large workloads based on massive or low requirements.
Use Cases for Batch Inference
- Document classification.
- Predict demand for inventory.
- Score transactions to detect fraud.
- Easy projection and suggestion of products to customers.
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