Models in production
long after the launch

Feature stores, training pipelines, model registries, monitoring and the operational discipline that keeps classical and modern models honest months after deployment. For ML engineers and platform leads.

Latest in MLOps

Feature Store Architecture: The Three Shapes

A feature store is a library, not a database. The three shapes that work in production and the engineering trade-offs that decide which one fits your team.

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MLOps

Feature Store Architecture: The Three Shapes

A feature store is a library, not a database. The three shapes that work in production and the engineering trade-offs that decide which one fits your team.

6 min read mlopsfeature-store
MLOps

Model Monitoring Beyond Accuracy Drift

Accuracy drift is the easy signal to monitor and the late one. The five signals that catch a degrading model before customers notice the regression.

5 min read mlopsmonitoring
MLOps

Shipping Classical ML in an LLM World

LLMs did not retire classical ML. The categories where gradient boosting and logistic regression still beat LLMs on cost, latency and reliability.

5 min read mlopsclassical-ml