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Monitoring ML Models in Production: A Complete Guide
A model that passed validation can fail quietly in production. This guide sets out what to monitor at each layer, how to cope with labels that arrive late and how to design alerts people act on, using a year of real sensor data in which the loudest alert was the least useful one.
Production ML Systems: Architecture and Best Practices
In a production machine learning system the model is the smallest part. This guide maps the components around it, compares the three serving patterns, explains the ML-specific technical debt that architecture has to contain, and sets out the practices that keep a system maintainable as it grows.
Model Packaging: From Pickle to Production
A trained model has to leave the notebook as a file, and the format decides how safe, portable and fast it is. This guide shows why loading a pickle can run arbitrary code, then saves one model as pickle, joblib, skops and ONNX and compares their size, their predictions and what each needs to load.
A/B Testing ML Models in Production
A model that wins offline still has to prove itself with real customers. On real bank marketing outcomes, this guide shows why a one-point offline gain needed about 36,000 calls per arm to confirm, how to split traffic and check the split, and how checking results early turned 4% false alarms into 18%.
Model Explainability in Production with SHAP
SHAP turns a prediction into per-feature contributions you can show a customer or an auditor. On 30,000 real credit card accounts, this guide produces reason codes for declined applicants, checks that explanations add up exactly, and shows an equally valid explainer setting changing the reasons for 42% of them.
FastAPI Guide: Build a Production API Step by Step
A step-by-step FastAPI build: request validation with Pydantic models, response models that control what leaves the API, dependency injection, proper status codes and errors, tests with TestClient and dependency overrides, then the settings, workers and health checks a production deployment needs.