Deploying models in the cloud after training

ML models can primarily be consumed in the cloud in two ways, batch inference and live inference. Batch inference refers to model inference performed on data that is in batches, often large batches, and asynchronous in nature. It fits use cases that collect data infrequently, that focus on group statistics rather than individual inference, and that do not need to have inference results right away for downstream processes. Projects that are research oriented, for example, do not require model inference to be returned for a data point right away. Researchers often collect a chunk of data for testing and evaluation purposes and care about overall statistics and performance rather than individual predictions. They can conduct the inference in batches and wait for the prediction for the whole batch to complete before they move on.

Live inference, on the other hand, refers to model inference performed in real time. It is expected that the inference...