I Squeezed an Entire MLOps Pipeline into 10 Lines of YAML

Every ML project I worked on in GitLab had the same problem: a bloated .gitlab-ci.yml with hand-rolled MLflow integration, custom validation scripts, and manual model registration. Copy it to the next project, tweak the paths, fix the bugs you already fixed last time. By the fifth project you don't remember which config has the working version of MLFLOW_RUN_ID passthrough between jobs. So I built a GitLab CI/CD component that replaces all of that with 10 lines of YAML. Before vs After Here's what a typical MLOps pipeline looked like before — and this is the shortened version: stages: [validate, train, evaluate, register] validate-data: stage: validate image: python:3.12 script: - pip install pandas great_expectations - python scripts/validate.py --data data/train.csv --check-nulls --threshold 0.05 artifacts: paths: [validation_report.json] train-model: stage: train image: python:3.12 variables: MLFLOW_TRACKING_URI: "${CI_API_V4_URL}/projects/${CI_PROJECT_ID}/ml/mlflow" script: - pip in