Offline Inference

Introduction

Machine learning is an area of artificial intelligence that involves the development of algorithms and models that can learn data and make predictions and decisions without being programmed. Inference is an important aspect of machine-learning. This is when a trained model is used to make predictions and/or decisions on new data.

Offline Inference

Offline inference, also known "batch-inference," is the use of a trained machine-learning model to make predictions and decisions on large datasets that are not being streamed in real time. The input data is usually stored in a file, or in a database. The model is run on the entire dataset at once, rather than being updated incrementally.

Offline inference has the advantage of processing large datasets in a way that is not possible in real-time. Offline inference can also be used to process data in batches and store the results for later analysis. This is useful when data is not required immediately, such for example when analysing customer data for marketing purposes.

Offline inference has a few drawbacks. It may take a lot of computational resources and the model may not adapt to changes in data distribution over time. Offline inference is not suitable for real-time applications that require rapid decision making.

Explain Like I'm 5 (ELI5)

Offline inference refers to when a computer program uses the "brain" it has learned from past data to make predictions about future data. However, it doesn't do it immediately, it does it all at one time, much like a test. This is useful when you have a lot information that you don’t need immediately but want to save for the future. It's like a teacher giving a test for the entire class. It takes a lot computer power to do this. The new information can't be changed if it hasn't learned the previous information.