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artificial intelligence objective checklist
Data Collection and Preprocessing
Acquire relevant data sources
Clean and preprocess data
Handle missing values and outliers
Normalize or standardize data
Model Selection and Training
Choose appropriate machine learning algorithm
Split data into training and testing sets
Train the model on the training set
Evaluate model performance on the testing set
Model Optimization and Tuning
Fine-tune hyperparameters
Use cross-validation techniques
Regularize the model to prevent overfitting
Experiment with different feature engineering techniques
Model Deployment and Monitoring
Deploy the model in a production environment
Monitor model performance and retrain as needed
Implement feedback mechanisms for continuous improvement
Ensure model compliance with ethical and legal standards
Interpretability and Explainability
Use interpretable models when possible
Provide explanations for model predictions
Conduct sensitivity analysis on model inputs
Communicate model limitations and uncertainties to stakeholders
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