MLOps
This section presents the end-to-end machine learning operations (MLOps) workflow, focusing on reproducibility, scalability, and efficient deployment. Projects explore containerization with Docker Compose, structured model training using PyTorch Lightning, hyperparameter tuning and experiment tracking, and real-time model serving using Gradio and Litserve.