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| Section | Weight | Objectives |
|---|---|---|
| Monitor Models in Production | 17% | - Production monitoring and drift detection - Bias and fairness evaluation - Model performance assessment - Maintenance and retraining strategy |
| Implement the Proper Machine Learning Model | 26% | - Model selection and validation - Supervised learning: Classification - Supervised learning: Regression - Unsupervised learning: Clustering - Unsupervised learning: Dimensionality reduction |
| Evaluate Business Problem Including Ethical Implications | 21% | - Identify ethical and compliance challenges - Apply AI design thinking - Follow the AI Ladder methodology - Understand business requirements - Assess available data and feasibility |
| Refine and Deploy the Model | 18% | - Feature engineering and selection - Model explainability and interpretability - Use IBM Watson Studio and related tools - Environment configuration and training setup - Hyperparameter optimization |
| Exploratory Data Analysis and Preparation | 18% | - Data visualization and statistical analysis - Data balancing, partitioning, and transformation - Data cleaning, labeling, and anonymization |
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