I’m moving a data-science prototype into a production-grade service and need practical help finishing the job. The work starts with translating existing notebooks into clean, modular Python code, then selecting and training the right algorithm—whether that ends up being XGBoost, TensorFlow, PyTorch, or classic scikit-learn models. Feature engineering, hyper-parameter tuning, and solid evaluation (AUC, F1, RMSE, etc.) will guide every choice. Once the model’s performance is locked in, the solution must be containerised, version-controlled in Git, covered by unit tests, and wired into a CI/CD workflow. The last step is a live deployment on a managed platform—AWS SageMaker, Google Vertex AI, or Azure ML—exposed through a REST endpoint so I can consume it from downstream services. Deliverables • A clean Git repository with reproducible training and inference code • Automated test suite and CI/CD pipeline (GitHub Actions or similar) • Docker image plus IaC scripts (CloudFormation, Terraform, or equivalent) • Running endpoint with basic monitoring and retraining hooks • Concise documentation covering setup, usage, and ongoing maintenance I’ll supply the data and cloud access as soon as we kick off and will stay closely involved for feedback on model selection and code reviews. If you’ve spent at least two years putting machine-learning systems into production and can point to similar work, let’s get started.