Deploying ML Models — Machine Learning Roadmap
Getting a trained model in front of real users and keeping it healthy
Steps in Deploying ML Models
- Serializing Models (Pickle/joblib/ONNX) — advanced · Saving a trained model to disk so it can be loaded and used later without retraining
- Serving Models via a REST API (FastAPI/Flask) — advanced · Wrapping a trained model in an HTTP endpoint other systems can call
- Containerizing ML Models with Docker — advanced · Packaging a model and its dependencies into a portable, reproducible container
- Batch vs Real-Time Inference — advanced · Choosing between scoring data in scheduled batches vs responding to individual requests
- Model Monitoring & Drift Detection — advanced · Detecting when a deployed model's performance degrades as the world changes
- A/B Testing for Models — advanced · Comparing a new model against the current one on real traffic before a full rollout
Part of
- Machine Learning roadmap — the full learning path