Artificial Intelligence & Machine Learning
AutoML
Definition
Automated Machine Learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. This includes tasks like feature engineering, model selection, hyperparameter tuning, and model deployment.
Why It Matters
AutoML aims to make machine learning more accessible to non-experts and to make data scientists more productive by automating the repetitive and time-consuming parts of the ML workflow.
Contextual Example
A business analyst could use an AutoML tool to upload a dataset, specify the target variable they want to predict, and have the tool automatically train and evaluate dozens of different models to find the best one, without needing to write any code.
Common Misunderstandings
- AutoML is not intended to completely replace data scientists, but to augment their capabilities.
- Neural Architecture Search (NAS) is a specific part of AutoML focused on deep learning.