Machine learning and deep learning model optimization remain a pivotal aspect of artificial intelligence development, encompassing crucial elements from data preprocessing to deployment monitoring. The optimization process involves multiple interconnected stages, including data quality management, algorithm selection, feature engineering, hyperparameter tuning, transfer learning, and model deployment strategies. Each stage presents unique challenges and opportunities for enhancing model performance, with modern techniques offering solutions for improved accuracy, efficiency, and reliability. From addressing data quality issues through systematic preprocessing to implementing sophisticated deployment monitoring systems, the various aspects of model optimization work together to create robust and effective machine learning solutions that can be successfully deployed in real-world applications.
Keywords: MLOps deployment, feature engineering, hyperparameter tuning, model optimization, transfer learning
