Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. Automated ML in Azure Machine Learning is based on a breakthrough from our Microsoft Research division.

Traditional machine learning model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. With automated machine learning, you'll accelerate the time it takes to get production-ready ML models with great ease and efficiency. [1]

Figure 1. Automated Machine Learning (AutoML)

Figure 1 shows AutoML is viewed as about algorithm selection, hyperparameter tuning of models, iterative modeling, and model evaluation. It is about making Machine Learning tasks easier to use less code and avoid hyper tuning manually.

The core innovation utilized in AutoML is hyperparameters search, utilized for preprocessing components and model type selection, and for optimizing their hyperparameters. [3]

AutoML provides tools to automatically discover good machine learning model pipelines for a dataset with very little user intervention. It is ideal for domain experts new to machine learning or machine learning practitioners looking to get good results quickly for a predictive modeling task. [4]

Examples of AutoML

Research in Automated Machine Learning is very diverse and brought up packages and methods targeted at both researchers and end users.

AutoML Systems

Throughout recent years several off-the-shelf packages have been developed which provide automated machine learning. We have developed:

• AutoWEKA is an approach for the simultaneous selection of a machine learning algorithm and its hyperparameters; combined with the WEKA package it automatically yields good models for a wide variety of data sets.
• Auto-sklearn is an extension of AutoWEKA using the Python library scikit-learn which is a drop-in replacement for regular scikit-learn classifiers and regressors.
• Auto-PyTorch is based on the deep learning framework PyTorch and jointly optimizes hyperparameters and the neural architecture. [2]

References:

1. https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml
2. https://www.automl.org/automl/
3. https://towardsdatascience.com/what-is-automl-6ddf27040f27
4. https://machinelearningmastery.com/automl-libraries-for-python/

Cite this article:

Thanusri swetha J (2022), Automated Machine Learning (AutoML), Anatechmaz, pp. 61