Machine learning (ML) and deep learning (DL) models are widely used across various industries, and improving their performance is crucial due to the increasing number of AI applications. Enhancements in ML models, like Amazon's shift from user-based to item-to-item collaborative filtering for product recommendations, have significantly impacted business outcomes. To optimize model performance, one must first identify areas for improvement by reviewing model hypotheses, performance, and potential errors through techniques such as hyperparameter optimization, feature engineering, and data quality refinement. Methods like grid search, random search, Bayesian optimization, and AutoML can help in tuning model hyperparameters, while addressing data issues can involve active learning, data augmentation, and generating synthetic samples. Additionally, using pre-trained models can save time and effort in model development. The process of ML model improvement involves a combination of algorithmic adjustments and data enhancements, and requires a systematic approach to achieve desired performance levels.