What is AutoML?
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. The automation potentially encompasses every stage from beginning with raw datasets through feature engineering, model selection, deployment, and solution monitoring. More specifically, AutoML automates the selection, composition, and parameterization of machine learning models.
Why it matters
By automating these processes, machine learning becomes more accessible and typically delivers faster, more accurate results compared to hand-coded algorithms. AutoML democratizes access to ML tools and handles time-consuming tuning tasks that would be impractical for humans to execute at scale.
See how Diwo operationalizes AutoML.
Read the decision-intelligence playbooks that put this concept to work at Fortune 50 scale.
Related concepts
Machine learning represents a data analytics approach that harnesses artificial intelligence to emulate human learning from experience. Rather than depending on preset formulas, machine learning algorithms extract "knowledge" directly from data through computational methods. These algorithms identify inherent patterns within datasets that generate actionable insights and support improved forecasting and decision-making.
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. AI employs sophisticated analysis and logic-based techniques—including machine learning—to interpret events, facilitate and automate decisions, and execute actions. This technology enables machines to understand, respond to, and learn with human-comparable levels of intelligence.
Explainable artificial intelligence (XAI) comprises processes and methods enabling users to comprehend and trust machine learning algorithm outputs. It describes AI models, their expected impact, and potential biases while characterizing model accuracy, fairness, transparency, and decision-making outcomes.
No-code represents an application development methodology that allows users to build application functionality without traditional programming. These frameworks enable users to define application design and workflow by connecting building blocks in a graphical interface, while selecting implementation details through menu-driven options.