3 docs tagged with "mlops-foundations"

How MLOps extends DevOps principles to handle the unique challenges of data, model quality, and concept drift that traditional software CI/CD cannot address.

The seven categories of hidden technical debt unique to machine learning systems - entanglement, hidden feedback loops, pipeline jungles, configuration debt, and how to detect and remediate them.

The complete end-to-end lifecycle of a machine learning model, from problem definition through deployment, monitoring, and eventual retirement - with feedback loops, governance, and retraining triggers.