Overview of AutoML Capabilities in Azure Machine Learning for Data Engineering

Citation: Sree Hari Subhash, "Overview of AutoML Capabilities in Azure Machine Learning for Data Engineering", Universal Library of Innovative Research and Studies, Volume 02, Issue 04.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article examines the constellation of AutoML capabilities within the Azure Machine Learning ecosystem from the vantage point of data engineering and the industrial integration of artificial intelligence into enterprise processes. The objective is a systematic analysis of the architecture and tooling of AutoML that enable the automation of model construction, interpretation, and deployment within an end-to-end MLOps pipeline. The topic’s relevance is driven by the rapid expansion of AI adoption and the heightened demands for reproducibility, transparency, and development velocity amid a shortage of qualified engineering talent. The novelty lies in construing AutoML not as an autonomous mechanism for model selection but as a holistic layer of technological data governance embedded in the enterprise digital infrastructure. The study undertakes a systematic analysis of Microsoft Learn technical documentation, sector reports by Gartner, Forrester, and McKinsey, and analytical publications on MLOps practices. On this basis, it is identified that AutoML in Azure Machine Learning implements a deterministic and reproducible automated learning process encompassing hyperparameter optimization, automatic feature engineering, model ensembling, and data-quality control. The principal conclusions are that Azure Machine Learning shapes a new paradigm of data engineering in which automation becomes the structuring principle. AutoML serves not merely to accelerate experimentation but to institutionalize trust in models, providing a balance among speed, transparency, and control. The platform’s technological ecosystem minimizes the gap between research and production environments, transforming artificial intelligence from a set of algorithms into a governed production system. The article will be helpful to data engineers, MLOps solution architects, researchers in automated machine learning, and professionals responsible for deploying enterprise AI platforms.

Keywords: AutoML, Azure Machine Learning, Data Engineering, MLOps, Model-Building Automation, Responsible AI, Interpretability, Reproducibility

https://doi.org/10.70315/uloap.ulirs.2025.0204013