Time to read: 4 minutes | Published: March 16, 2025
AutoML What is AutoML?
Automatic Machine Learning (AutoML) simplifies machine learning models for non-experts. AutoML automates creating and deploying machine learning algorithms for corporate and personal usage. Data preparation, feature selection, model selection, hyperparameter tweaking, and model assessment are automated, saving time and expertise to construct successful AI models. AutoML solutions make AI accessible to enterprises, researchers, and developers without extensive ML understanding, democratizing AI.
- AutoML Process
- Benefits of AutoML
- Partner with HPE
AutoML Process
Process breakdown of AutoML
Problem definition: Identify the problem and set a goal before using machine learning.
- Define the issue: Select the model's task, such as classification, regression, clustering, or anomaly detection. Knowing the challenge helps choose the correct ML strategy.
- Define the goal: Define success measurements and results. Accuracy, precision, recall, RMSE, and business-specific KPIs are examples.
Data preparation: ML models depend on good data. Data is collected, cleaned, and transformed for best performance.
- Data collection: Gather necessary datasets from databases, APIs, logs, and other sources. Data quality and amount affect model performance.
- Data cleaning: Remove duplicates, outliers, and missing values to maintain dataset consistency. This stage gives the model accurate, dependable data to learn from.
- Feature engineering: Transform, combine, or choose key variables to create significant characteristics. Normalisation, encoding categorical variables, and data analysis can yield fresh insights.
- Data splitting: Split the dataset into training, validation, and test sets. An 80-10-10 or 70-15-15 split is employed for optimal model training and evaluation .
Model selection: Perfect performance requires the proper algorithm.
- Search space: Define AutoML's search space, which may include decision trees, neural networks, and SVMs.
- A model architecture: Determine model structure, such as deep learning layers, decision tree depth, or neural network activation functions.
Hyperparameter optimization: Optimize hyperparameters to increase model performance and generalization.
- Hyperparameters: Determine model training hyperparameters such learning rate, layer count, batch size, and regularization parameters.
- Strategies for optimization: Grid Search, Random Search, and Bayesian Optimization automatically optimise hyperparameters for optimum outcomes.
Training and evaluation: This ensures the model learns and is assessed accurately.
- Model training: Use the training dataset to teach the model historical patterns.
- Model evaluation: Use accuracy, precision, recall, F1-score, MAE, or RMSE to evaluate model performance.
- Cross-validation: Use k-fold cross-validation to guarantee that the model generalizes effectively to new data and is not overfitted.
Model selection and ensemble: After training, the best models are chosen and integrated for improved outcomes.
- Best model choice: Select the best model from evaluation metrics and validation findings.
- Ensembling: Use bagging, boosting, and stacking models to enhance accuracy and minimize variation. Common approaches include Random Forest, XGBoost, and mixing.
Model deployment: After choosing the best model, deploy and monitor it in real life.
- Final evaluation: Test the test dataset again before deployment to validate performance.
- Deployment: Deploy the model as an API, web service, or embedded system for real-time predictions. We can use cloud platforms, edge devices, or on-premise servers.
- Monitoring: Monitor model performance, discover data drift, and update or retrain the model as needed to maintain accuracy.
This organized AutoML approach enables rapid, optimal, and scalable machine learning model deployment with minimal user involvement.
