AutoML
AutoML (Automated Machine Learning) is a set of tools and techniques that automate the process of applying machine learning to real-world problems. It handles tasks such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and model evaluation, reducing the need for manual intervention by data scientists. This enables faster development and deployment of machine learning models, making ML more accessible to non-experts.
Developers should learn AutoML when they need to build machine learning models quickly without deep expertise in ML algorithms or when working on projects with tight deadlines. It is particularly useful for prototyping, automating repetitive ML workflows, and enabling domain experts (e.g., in business or healthcare) to leverage ML without extensive coding. Use cases include predictive analytics, image classification, and natural language processing tasks where efficiency is critical.