Model optimization is the process of improving a machine learning model so it delivers better results while using computing resources efficiently. It can involve adjusting model parameters, selecting useful features, improving training methods, reducing unnecessary complexity, or choosing a more suitable model architecture. The goal is often to balance accuracy, speed, resource use, and reliability for a specific task.
For example, a company may optimize an AI model used for customer support so it responds accurately while requiring less computing power. This can reduce operating costs, improve response times, and support larger workloads. Model optimization is useful for applications such as recommendation systems, forecasting, computer vision, and language-based AI.
