Deliberative discussions play a critical role in shaping public opinion, yet current machine-learning models for predicting opinion change remain difficult to reproduce, scale, and compare across studies. Although prior research has shown that transformer-based and neural network architectures can capture linguistic and behavioral signals associated with opinion shifts, these studies are typically limited by incomplete data pipelines, inconsistent preprocessing, and a lack of standardized experimental infrastructure. This project proposes and implements a cloud-based Machine Learning Operations (MLOps) platform designed to automate, scale, and improve the reproducibility of machine-learning experiments for predicting opinion shifts within deliberative conversations. Using large-scale deliberation datasets provided by the Deliberative Democracy Lab, the platform integrates data preprocessing, feature engineering, text-embedding storage, model training, evaluation, and version tracking into a unified and automated pipeline built on Amazon SageMaker and DynamoDB. Structured features are managed through SageMaker Feature Store, while high-dimensional textual embeddings are stored and queried using a custom vector database architecture. PyTorch-based models, including neural and transformer-based architectures, are trained and evaluated through SageMaker Pipelines. The platform quantitatively evaluates model performance using accuracy, stability, and robustness metrics, supported by cross-validation and bootstrapping to mitigate overfitting and small-sample bias. Results are benchmarked against prior deliberation-prediction studies to assess whether automated MLOps workflows improve both predictive performance and experimental reproducibility. The final outcome is an MLOps system and a research paper that demonstrates how scalable infrastructure can quantitatively advance the study of opinion dynamics and cognitive science in structured dialogue.