- Machine Learning (ML) system design involves the process of creating and deploying ML models to solve real-world problems.
- It requires careful planning, consideration of various factors, and expertise in ML algorithms, data engineering, and software development.
A typical ML system consists of the following components:
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Data Acquisition and Preparation:
- Data Collection: Gathering relevant data from various sources.
- Data Cleaning: Handling missing values, outliers, and inconsistencies.
- Data Transformation: Converting data into a suitable format for ML algorithms.
- Feature Engineering: Creating new features or transforming existing ones to improve model performance.
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Model Selection and Training:
- Algorithm Choice: Selecting appropriate ML algorithms based on the problem type and data characteristics.
- Model Training: Feeding the prepared data into the chosen algorithm to learn patterns and relationships.
- Hyperparameter Tuning: Optimizing model parameters to achieve the best performance.
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Model Evaluation:
- Performance Metrics: Assessing model accuracy, precision, recall, F1-score, and other relevant metrics.
- Cross-Validation: Evaluating model performance on unseen data to prevent overfitting.
- Bias and Fairness: Ensuring the model is unbiased and fair in its predictions.
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Deployment:
- Integration: Integrating the trained model into the target application or system.
- Monitoring: Continuously tracking model performance and identifying issues.
- Retraining: Updating the model with new data to maintain accuracy over time.