AutoML systems can generate human-readable code for their models, but this capability varies depending on the platform and the level of customization involved. Many AutoML tools, such as Google’s AutoML Tables or H2O.ai’s Driverless AI, provide export options that produce code snippets or full scripts in languages like Python. These exports often include preprocessing steps, model architecture definitions, and even deployment code. For example, a tool might generate a Python script using TensorFlow or scikit-learn that defines a trained model’s structure, allowing developers to inspect or modify it. However, the readability of this code depends on how the AutoML tool structures its output—some prioritize simplicity for end-users, while others produce verbose, framework-specific code.
The generated code is typically functional but may lack the elegance or efficiency of hand-written implementations. AutoML tools often abstract complex steps to simplify workflows, which can result in code that includes unnecessary boilerplate or hardcoded parameters. For instance, a script might load a preprocessed dataset from a specific file path without explaining how the preprocessing was done, making it harder to adapt to new data. Additionally, some platforms generate code tightly coupled with proprietary libraries, limiting portability. Tools like DataRobot address this by offering code exports in open-source frameworks (e.g., Python’s XGBoost), but even then, the code might not follow best practices like modular functions or clear documentation. Developers may need to refactor or annotate the code to improve readability or integrate it into existing systems.
Despite these limitations, AutoML-generated code serves practical purposes. It provides a starting point for developers to understand how a model works, debug issues, or extend functionality. For example, if an AutoML tool generates a PyTorch model class, a developer could modify layers or activation functions to experiment with improvements. The code also aids in deployment: platforms like Azure AutoML can generate Dockerfiles or scoring scripts tailored to cloud services, reducing the effort required to operationalize models. While the code may not be production-ready out of the box, it bridges the gap between automated training and manual refinement, enabling teams to leverage AutoML’s speed without sacrificing control over the final implementation.