Build an ML app pipeline with GitLab Model Registry using MLflow

What this post added

This post details the technical steps and considerations for setting up an MLOps pipeline using GitLab Model Registry and MLflow. It covers environment variable configuration for MLflow tracking, logging model parameters and artifacts within merge request pipelines, registering the most successful model candidate, and Dockerizing and deploying the ML application using the registered model and GitLab Container Registry. It also highlights the use of CI/CD components for reusability.