AutoML-Pipeline: A RAG-Enhanced Code Generation Framework With Pre-Validation for Cloud-Native Machine Learning Workflows

The proliferation of cloud-native machine learning platforms has significantly accelerated model development and deployment cycles. However, constructing and maintaining heterogeneous pipeline code spanning multiple languages (Python, YAML, Spark SQL) and cloud-specific configurations remains labor-intensive and error-prone. Existing LLM-based code generation tools lack awareness of runtime constraints and historical execution patterns, frequently producing code with resource misconfigurations or dependency conflicts that fail upon deployment. To address these challenges, we propose AutoML-Pipeline, a closed-loop code generation framework that integrates Retrieval-Augmented Generation (RAG) with reinforcement…