AutoML (Automated Machine Learning) is a technology that automates the entire machine learning workflow, from model selection and feature engineering to hyperparameter tuning and evaluation.

This makes machine learning accessible to non-experts and allows analysts to build predictive models without manually writing Python code.

AutoML systems perform tasks such as:

• Data cleaning

• Feature selection

• Feature engineering

• Model comparison

• Hyperparameter optimization

• Cross-validation

• Performance scoring

• Deployment packaging

Popular AutoML tools include: Google AutoML, Azure AutoML, H2O.ai, AWS SageMaker Autopilot, and no-code platforms inside BI tools like Qlik AutoML and Tableau.

Technically, AutoML works by running hundreds of model candidates, random forests, gradient boosting, neural networks, logistic regression, and comparing their performance on the selected metric (accuracy, RMSE, F1-score, etc.).

AutoML is useful for:

• Churn prediction

• Lead scoring

• Demand forecasting

• Fraud detection

• Anomaly detection

• Marketing attribution

• Revenue optimization

The benefit is speed: instead of taking weeks to build models, analysts can build them in hours. The downside is a lack of control; AutoML can produce black-box models that are hard to interpret.

Modern AutoML tools increasingly include explainable AI, allowing users to understand which features drive predictions.

AutoML has become a key addition to BI because it extends analytics from descriptive (“what happened?”) to predictive (“what will happen?”) and prescriptive (“what should we do?”). For companies without data science teams, AutoML democratizes machine learning.