I’m ready to move beyond notebooks and actually ship models, but I’d like to do it the right way, step by step, with an expert by my side. My stack of choice is Python, and we’ll be working on Google Cloud. The part I’m most eager to master is model serving and building production-grade APIs, though I still want you to frame everything in an end-to-end workflow so the pieces fit together. Here’s how I picture our work together: • Pair-programming sessions where we containerise a trained model, push it to Google Cloud (e.g., Cloud Run or Vertex AI), and expose it through a REST or gRPC endpoint. • Walk-through of best practices for versioning, environment management, and automated CI/CD so each new model can be rolled out with minimal friction. • Real-world example project we build from scratch: data prep, training, packaging, deployment, and a quick monitoring stub, with code hosted in a clean Git repo I can reference later. • Short homework tasks between calls so I cement what we covered and come prepared with questions. If you have experience turning Jupyter prototypes into scalable production services on GCP using Python frameworks such as FastAPI, Flask, or TensorFlow Serving, I’d love to hear how you can guide me. Clear explanations, screen-sharing while you code, and the ability to leave me with reusable scripts or templates are essential.