CI/CD for ML
Continuous Integration and Continuous Deployment applied to machine learning — automating the testing, validation, and deployment of ML models whenever code or data changes.
Why It Matters
CI/CD for ML closes the gap between ML experiments and production. It catches model quality regressions before they reach users.
Example
A pipeline that automatically retrains a model when new data arrives, runs evaluation tests, compares performance to the current production model, and deploys if it wins.
Think of it like...
Like software CI/CD but with additional steps — not just 'does the code compile?' but 'is the model still accurate?' and 'has the data changed?'
Related Terms
MLOps
Machine Learning Operations — the set of practices that combine ML, DevOps, and data engineering to deploy and maintain ML models in production reliably and efficiently.
Deployment
The process of making a trained ML model available for use in production applications. Deployment involves packaging the model, setting up serving infrastructure, and establishing monitoring.
Model Registry
A centralized repository for storing, versioning, and managing trained ML models along with their metadata (metrics, parameters, lineage). It serves as the system of record for models.