The final installment of the three‑part MLOps series moves the Adire generator from prototype to product. A Gradio front‑end lets users type prompts and receive Nigerian‑style artwork in seconds, while every request streams parameters and latency metrics to MLflow for instant diagnostics. This UI replaces the earlier terminal workflow.

Deployment lands on HuggingFace, where the model weights and a detailed Model Card serve as an app‑store listing. A GitHub CI/CD pipeline runs unit tests, validates that the new checkpoint still hits the 0.75 quality threshold, and only then pushes updates to the live service. Automated checks keep bugs from reaching users.

Production monitoring uses a custom dashboard that flags average generation times above thirty seconds, alerting engineers to potential drift or GPU throttling. Performance gains stem from torch.compile, attention slicing and xFormers, roughly doubling throughput and halving cloud costs. Next steps include scaling the service and gathering user feedback to refine the Adire style.