ADS

The Process of ADS

The process of Automated Data Science typically involves several key steps:

• 1. Data Collection and Integration: Gathering data from various sources. Integrating and consolidating data into a usable format.
• 2. Data Cleaning and Preprocessing: Handling missing values, outliers, and noisy data. Normalizing and transforming data to prepare it for analysis.
• 3. Feature Engineering: Identifying and creating relevant features that will be used for model training. Automating the feature selection process to choose the most impactful features.
• 4. Model Selection: Using automated tools to select the best algorithm for the given data and problem. Comparing multiple models based on performance metrics.
• 5. Model Training and Validation: Training models on the dataset. Validating models using techniques like cross-validation to ensure they generalize well to new data.
• 6. Hyperparameter Tuning: Automatically adjusting model parameters to optimize performance. Using techniques like grid search or random search for hyperparameter optimization.
• 7. Model Evaluation: Evaluating models based on various performance metrics (e.g., accuracy, precision, recall). Ensuring the model meets the desired criteria before deployment.
• 8. Model Deployment: Deploying the model into a production environment. Setting up monitoring to track the model’s performance over time.
• 9. Model Maintenance: Regularly updating the model with new data. Retraining and fine-tuning the model as needed to maintain accuracy and relevance.