Automated Machine Learning (AutoML)

Applied AI can be time-consuming, resource-intensive, and challenging. Automated Machine Learning (AutoML) seeks to automate the many cumbersome and repetitive steps of the machine learning pipeline to make it easier to apply machine learning methods to real-world business problems.

These are the typical steps that can be automated after the target variable and evaluation metric criteria have been determined:

  1. Data pre-processing

  2. Data partitioning

  3. Feature extraction

  4. Algorithm selection

  5. Training

  6. Tuning

  7. Ensembling

  8. Deployment

  9. Monitoring

AutoML generally speaking is defined as the process of selecting the combination of algorithm and parameters that collectively produce the best performing model automatically.

AutoML is a technology that people with limited machine learning expertise (sometimes referred to as Citizen Data Scientists) can leverage to produce state-of-the-art models. Some AutoML tools are “drag-and-drop” or "no code" in that the user can simply upload a dataset and get a trained model. Other more advanced tools are used to free data scientists from the burden of repetitive and time-consuming tasks such as pipeline design and hyperparameter optimization -- but require expertise in machine learning.

AutoML leverages transfer learning, neural architecture search, and other toolsets to determine the best performing model.