Access and Persist MLflow running in Google Colab

Conducting experiments for ML projects often requires a lot of feedback loops and changes at almost every step of the process. This often involves tuning a large number of parameters, adding new features, or even deleting existing ones, as well as handcrafting more sophisticated features. Every model also comes with a myriad of additional metadata. Keeping notes of everything manually would be a…