Model Optimization

Model optimization refers to the process of refining and improving the performance of machine learning models by adjusting their parameters, architecture, or training data to achieve better accuracy, efficiency, or scalability. As AI and machine learning continue to drive innovation in various industries, model optimization has become a crucial aspect of research, enabling developers to deploy more effective and reliable models in real-world applications, from computer vision and natural language processing to recommender systems and predictive analytics.