What Is Model Drift and Why Does It Sink Production ML?
Data drift, concept drift, model decay — these terms get used interchangeably but they describe different failure modes. Here's how to tell them apart.
From the team
ML engineering insights from practitioners
Written by engineers who've operated models in production. No sponsored content. No thought leadership. Just things that actually happened.
Articles
Model Versioning Best Practices: What Git Taught Us and What It Didn't
Git handles code versioning beautifully. Model weights are a different problem — binary blobs, metadata coupling, lineage tracking, rollback semantics.
Retraining Triggers Explained: Metric Threshold, Drift Score, or Schedule?
There are three ways to trigger a retraining run, and each fits a different failure mode. Using the wrong trigger type means either over-training or under-training.
CI/CD for ML Models: Adapting Software Delivery Principles to Model Pipelines
Software CI/CD is a solved problem. ML CI/CD is still a mess. This guide maps the classic pipeline stages to their ML equivalents and shows where the seams break.
MLOps for Small Teams: What to Automate First When You Have Three People
You don't need a six-person platform team to run models in production reliably. But you do need to pick the right two or three things to automate.
The Production ML Monitoring Checklist: 12 Things to Watch Before Your Users Notice
Most teams monitor model accuracy. Far fewer monitor input distribution shift, feature coverage, prediction latency percentiles, or retraining queue depth.