training

AutoML

Automated Machine Learning, tools and techniques that automate the process of applying machine learning, including feature engineering, model selection, and hyperparameter tuning. AutoML makes ML accessible to non-experts and speeds up model development.

In practice

Google's AutoML can automatically design neural network architectures that outperform hand-designed ones on specific tasks.

Hyperparameter Tuning
The process of optimizing the configuration settings that govern the training process, such as learning rate, batch size, and number of layers. Unlike model parameters, hyperparameters are set before training and significantly impact model performance.
Machine Learning
A branch of artificial intelligence where systems learn patterns from data rather than being explicitly programmed. ML algorithms improve their performance on tasks through experience, using statistical methods to find patterns in training data.
Feature Engineering
The process of selecting, transforming, and creating input variables (features) from raw data to improve model performance. Good feature engineering can significantly boost model accuracy and is often more impactful than model architecture changes.

In the index

Tools that mention AutoML

DataRobotTrust 84
H2O.aiTrust 83

Matched on each tool’s own description and feature list, highest trust score first.