S
- Sandbox Environments
- Secure Coding Practices
- Security Automation
- Security Awareness Training
- Security Champions
- Security Information and Event Management (SIEM)
- Security Orchestration
- Security Posture
- Shift-Left Security
- Smart City
- Smart Home
- Smart Manufacturing
- Smart Meters
- Smart Products
- Smart Spaces
- Software as a Service (SaaS)
- Software Composition Analysis (SCA)
- Software Defined Networking (SDN)
- Software Development Life Cycle (SDLC)
- Static Application Security Testing (SAST)
- Structured Data
Automated Machine Learning
Simple Definition for Beginners
Automated machine learning (AutoML) is the use of smart computer programs to ease and accelerate the building and using of machine learning models, even for people who are not experts.
Common Use Example
A marketing team wants to predict which customers are most likely to buy a new product. They use an AutoML tool that automatically selects the best algorithms and builds a machine learning model to make these predictions.
Technical Definition for Professionals
Automated machine learning (AutoML) refers to the process of automating the end-to-end process of applying machine learning to real-world problems. This includes tasks such as data preprocessing, feature selection, model selection, hyperparameter tuning, and model evaluation.
AutoML tools make machine learning accessible to non-experts by reducing the need for deep knowledge of machine learning algorithms and programming. These tools use
advanced algorithms and techniques to automate repetitive and complex tasks, ensuring that the best possible model is generated based on the given data.
Automated Machine Learning