AutoML (Automated Machine Learning) is a set of techniques and tools that automate the end-to-end process of applying machine learning to real-world problems. Traditionally, building a machine learning model involves various steps, such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and model deployment. These tasks require significant expertise and time, making it challenging for non-experts to develop high-performing models. AutoML simplifies and automates these processes, enabling users to develop models with minimal intervention while ensuring high performance.
AutoML frameworks—such as Google Cloud AutoML, H2O.ai, Auto-sklearn, and TPOT—allow users to focus on problem definition and data while the system handles the rest. Let's walk through how AutoML works using code snippets based on the Python library Auto-sklearn, one of the most popular AutoML libraries.

Step-by-Step AutoML Process with Code Snippets

Step 1: Problem Definition and Data Collection

The first step is to define the problem and load the dataset. Let's assume you're working with the famous Iris dataset, which is used for classification problems.

We load the dataset and split it into training and testing sets. In a real-world scenario, you would collect and prepare your dataset based on your problem domain.

Step 2: Data Preprocessing

In most machine learning workflows, data preprocessing involves handling missing values, scaling, and encoding categorical variables. With AutoML, most of this process is automated.
Auto-sklearn, for example, automatically handles preprocessing for you. However, if needed, you can still perform basic preprocessing manually.

This step shows how to manually scale features, though AutoML frameworks perform this step automatically.

Step 3: Feature Selection (Optional)

AutoML frameworks automatically select the most relevant features during model selection. However, you could implement manual feature selection if desired.

In AutoML workflows, feature selection is performed internally based on the model chosen and the dataset's characteristics.

Step 4: Model Selection with AutoML

Now, let’s move on to the actual AutoML process. This step involves letting AutoML try different machine-learning models and configurations.
Here’s how you can automatically use Auto-sklearn to select the best model for the classification task.

In this code snippet, we initialize an Auto-sklearn classifier. The time_left_for_this_task parameter determines the total time allocated for the AutoML process (e.g., 5 minutes), while per_run_time_limit specifies how long each model has to train.

Step 5: Hyperparameter Optimization

AutoML also automates hyperparameter tuning. The AutoML system tries different models and optimizes their hyperparameters to find the best-performing one.
With Auto-sklearn, you don’t need to define this process explicitly. It automatically performs hyperparameter optimization as part of the search.

Step 6: Model Training and Ensemble Learning

AutoML will evaluate multiple models during training and often combine them through ensemble learning to improve overall performance.

This will print the ensemble of models that Auto-sklearn has built. The framework often combines several models to create a stronger prediction mechanism.

Step 7: Model Evaluation

Once the models are trained, you can evaluate the AutoML pipeline's performance on the test set. AutoML frameworks typically provide easy access to evaluation metrics such as accuracy, precision, recall, and F1 Score.

In this example, we use the accuracy_score metric to evaluate how well the model performs on the test set. Depending on the problem, you could also use metrics such as F1-score or ROC-AUC for evaluation.

Step 8: Model Deployment

Once a model has been trained and evaluated, it can be saved and deployed in production environments. AutoML frameworks often provide utilities for saving and exporting models.

The model can be exported using joblib or similar libraries, allowing it to be used in production systems or integrated with other services.

Conclusion

AutoML dramatically simplifies the process of building and deploying machine learning models. By automating steps such as data preprocessing, model selection, hyperparameter tuning, and ensemble learning, AutoML enables even non-experts to quickly and effectively apply machine learning to their problems.
Auto-sklearn is just one of many AutoML frameworks that provide this functionality, but the core steps are similar across tools. Whether you're building simple models or complex, large-scale solutions, AutoML can streamline the process and help ensure you're using the best possible model for your data.