Automated Machine Learning, commonly known as AutoML, has emerged as a critical paradigm for accelerating the development and deployment of machine learning systems. By automating tasks such as feature engineering, model selection, hyperparameter tuning, and evaluation, AutoML reduces the barrier to entry while improving efficiency for experienced practitioners. However, moving AutoML from experimentation into production introduces a new layer of complexity that requires robust system design, governance, and monitoring. At its core, AutoML builds upon techniques from Machine Learning to automate the end-to-end modeling pipeline. Traditional workflows involve manual experimentation with multiple algorithms and configurations, which is time-consuming and resource-intensive. AutoML systems leverage search strategies such as grid search, random search, and more advanced methods like Bayesian optimization to explore the model space efficiently.…