Inference Optimization in GenAI Systems

Machine learning (ML) models are trained to make predictions and generate output based on some input. Inference in ML refers to providing live data or information to a trained model to see how it recognizes patterns, makes predictions, or solves a task. Inference lets one know how well a model responds to new data after training. This may include testing the speed of the inference or evaluating the model’s outputs.

Assuming you are satisfied with the output it generates through Inference, the next step is to scale it and impress upon the users how well you developed and trained the model. That’s where the challenge lies because the users are concerned with the accuracy and overall performance, such as latency, availability, and scalability, not to mention the cost and energy metrics at the service provider’s end.

What and why of inference optimization

Inference optimization is the process of optimizing inference. i.e., improving the speed, scale, and efficiency of the AI system without compromising on the accuracy of the result. This is necessary in building services for production environments to handle large user traffic, especially during peak hours.