10 Machine Learning Design Patterns that every AI developer should keep in mind when designing an AI system

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1. Model Selection
This pattern involves selecting the most appropriate model architecture for the specific problem and data at hand. This involves a careful analysis of the available data, the features that are important, and the model's complexity.

2. Data Cleaning
This pattern involves cleaning and preprocessing the data to remove noise, fill in missing values, and transform the data into a format that can be used for training the model

3. Feature Engineering
This pattern involves creating new features from the existing data, such as calculating statistical measures, transforming data types, and creating new variables from existing ones

4. Hyperparameter Tuning
This pattern involves selecting the optimal values for the hyperparameters of the model, such as learning rate, regularization, and the number of layers

5. Cross-Validation
This pattern involves splitting the data into multiple training and validation sets to prevent overfitting and evaluate the model's performance

6. Ensembling
This pattern involves combining multiple models to achieve better accuracy and reduce variance.

7. Transfer Learning
This pattern involves leveraging a pre-trained model on a similar task to improve performance on a new task

8. Incremental Learning
This pattern involves continuously updating and refining the model as new data becomes available

9. Active Learning
This pattern involves selecting the most informative samples for labeling to improve the model's performance with minimal human input.

10 Interpretability
This pattern involves designing models that are transparent and explainable, allowing users to understand how the model makes predictions and decisions