Build an ML app pipeline with GitLab Model Registry using MLflow

This post introduces a tutorial on building an ML app pipeline using GitLab Model Registry and MLflow. It details setting up environment variables for MLflow tracking, training and logging model candidates within merge requests, registering the most successful candidate, and Dockerizing/deploying the ML app. The post highlights the use of CI/CD components for reusability and the integration of MLflow with GitLab's project structure for model experiments. It also mentions the use of Random Forest Classifier, Decision Tree, and Logistic Regression for a loan prediction example.