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MLOps Guides & Tutorials
Operationalise machine learning — MLflow, Docker, Kubernetes, CI/CD, monitoring and production ML system design.
MLOps articles
GitHub Actions for ML: Automating Workflows
GitHub Actions can test, train and gate a model on every change. This guide writes a complete workflow for a real concrete-strength model, runs its test and training steps locally, and lints it with actionlint, which caught four of five planted mistakes and missed one that only a careful review would find.
Model Drift and Data Drift: Detection and Response
Most drift tooling watches a model's inputs. This guide shows, on three Januaries of real New York taxi trips, a change that left every input statistic flat while the model's error grew fivefold, why significance tests mislead at scale, and how to respond to drift you detect and to change you know is coming.
Edge ML Deployment: Running Models on Devices
Edge deployment runs models on phones and devices instead of servers. With real smartphone sensor data, this guide exports an activity model to ONNX, quantizes it to a 39 KB file at almost no accuracy cost, and shows the bigger risk on devices is new people: accuracy ranged from 81% to 99.5% across volunteers.
Containerizing a Machine Learning Model Step by Step
Most container tutorials stop when the container starts. This walkthrough containerizes a real scikit-learn model step by step, then measures what matters: build and rebuild times, image size, the user it runs as, time to healthy, memory, and whether the container's predictions match training exactly.
MLOps Interview Questions and Answers, Part 2: The ML System Design Round
In the ML system design round, interviewers want numbers, not boxes. This guide works one design question end to end: a cancellation-risk service for hotel bookings. It checks the label and leakage, compares three models on quality, size and latency, and sizes the serving fleet from measurements rather than guesses.
Pipeline Orchestration: Airflow vs Prefect vs Dagster
Machine learning pipelines ask more of an orchestrator than nightly ETL: skipping expensive steps that have not changed, training many segment models with retries, and knowing which model came from which data. This guide runs those needs in Prefect and Dagster on real data, and maps them to Airflow.