Automated Machine Learning (AutoML) refers to the process of automating the end-to-end process of applying machine learning to real-world problems. The goal of AutoML is to make machine learning accessible to non-experts and to improve the efficiency of experts by automating tasks that would traditionally require manual intervention.
Key components of AutoML include:
Data Preprocessing
Automatically cleaning, transforming, and structuring raw data into a suitable format for modeling.
Model Selection
AutoML systems automatically search for the most appropriate machine learning model for a given dataset and problem, often trying various algorithms and hyperparameters.
Feature Engineering
Automatically selecting and creating the best features from raw data, which are essential for improving model performance.
Hyperparameter Tuning
Optimizing the hyperparameters of machine learning models to improve their performance, usually through methods like grid search or Bayesian optimization.
Model Training & Evaluation
AutoML platforms manage the process of training models, evaluating their performance, and selecting the best-performing models.
Deployment
Some AutoML systems also help automate the deployment of models into production environments.
AutoML simplifies and accelerates the process of building machine learning models, making it more efficient and accessible, even for those with limited expertise in data science or machine learning. Popular AutoML frameworks include Google Cloud AutoML, H2O.ai, and Microsoft Azure AutoML.