AutoML refers to techniques for automatically discovering the best-performing model for a given dataset.
When applied to neural networks, this involves both discovering the model architecture and the hyperparameters used to train the model, generally referred to as neural architecture search.
Hyperparameter optimization (HO) is the technique of finding the optimal values of different model hyperparameters such as learning rate, etc
Automated Machine Learning (AutoML) helps in automating some critical components of the machine learning pipeline. This machine learning pipeline consists of data understanding, data engineering, feature engineering, model training, hyperparameter tuning, model monitoring, etc.
In simple words is a way of automating the tasks:
- Preprocess and clean the data.
- Select and construct appropriate features.
- Select an appropriate model family.
- Optimize model hyperparameters.
- Postprocess machine learning models.
- Critically analyze the results obtained.