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5 hands-on guides tagged Python — real projects, real infrastructure, zero fluff.
Muhammad Rashid10 min readA model can fail with no code change at all: the world moves on and your frozen model goes stale. Detect data and concept drift with a KS test, and build a retraining pipeline that only ships a new model if it beats the current one. Part 10 of the MLOps series.
Muhammad Rashid11 min readStop overwriting model.joblib. Track every experiment's params and metrics, register versions, and promote by a champion/challenger gate with MLflow, so 'which model is in production, and is it actually better' has an answer. Part 5 of the MLOps series.
Muhammad Rashid10 min readPackage the model, the exact scikit-learn version, and the API into one image that runs the same everywhere. Multi-stage builds, a lean non-root image, .dockerignore, and Docker Compose. Part 3 of the MLOps series.
Muhammad Rashid10 min readTurn a model artifact into a real prediction API — request validation with Pydantic, automatic Swagger docs, real-time vs batch inference, and a Docker-ready project structure. Part 2 of the MLOps series.
Muhammad Rashid9 min readMLOps explained for DevOps engineers — what it is, why plain DevOps isn't enough for ML, and a hands-on look at training vs inference using a real scikit-learn model. Part 1 of an 11-part series.
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