Ludwig v0.5.0 has expanded its capabilities from tabular datasets to text classification, leveraging AutoML to automatically create deep learning models given a dataset, label column, and time budget. This open-source framework uses pre-trained models like BERT-base to simplify the model search process while addressing the challenges of managing resources for text models. Through extensive experimentation and heuristic development, Ludwig AutoML is designed to produce models with an accuracy within 2% of those manually tuned by experts. The system is validated on diverse datasets and incorporates features like memory tuning and step-based evaluation to optimize performance under resource constraints. The framework aims to make deep learning accessible and iterative, with ongoing developments to extend its applications further, such as into image classification, while fostering community engagement and contributions.