Automated Machine Learning

Automated Machine Learning (AutoML) is the process of automating Modeling Processes.

Modeling Process Automation

 The specifics of AutoML process automation depend on the platform being used, such as:

Within typical Modeling Processes, and depending on the platform, at least some of the processes below are fully or partially automated:

  • Data Collection

  • Model Algorithm Selection

  • Model Hyperparameters Setting

  • Model Training

  • Model Testing

  • Model Evaluation

  • Model Deployment

Purpose of Process Automation

Purposes include:

  • Improving the accessibility of Machine Learning

  • Reducing the Learning Curve for Machine Learning

  • Providing Quick Start Capabilities

Methods of Process Automation

Methods include:

  • Sample Data Access

    • Providing Sample Data for Initial Processing and Learning

  • Simplification of Processes

    • Automating Parts of Processes

    • Providing Simplified Explanations of Technologies

  • Automatic Option Selection

    • Suggesting the Best Option

    • Automatic Selection with Options for Change

  • Automatic Processing

    • Process Initiation

    • Process Tracking

    • Process Completion Notification

References