Machine Learning System Design Interview Overview: Design and implement a machine learning system that meets specific business requirements. Demonstrate understanding of ML algorithms, data preprocessing, model evaluation, and deployment. Key Product Details: Data Preprocessing: Handle missing values, outliers, and data normalization. Feature engineering and selection to optimize model performance. Model Selection and Training: Evaluate different ML algorithms (e.g., regression, classification, clustering). Train models using appropriate hyperparameters and optimization techniques. Model Evaluation: Use metrics such as accuracy, precision, recall, and F1-score. Perform cross-validation and hyperparameter tuning to improve model performance. Deployment and Monitoring: Deploy models in a production environment. Monitor model performance and retrain as needed to maintain accuracy. Communication and Presentation: Clearly articulate the design and implementation of the ML system. * Present results and insights effectively to stakeholders.