Get to know DAGsHub interactively.
In the first part of this session, we will explore DAGsHub using a project called "SavtaDepth". we will learn what DAGsHub is and how it can help us manage our project efficiently and scale our work.
The second part will cover two of DAGsHub's primary tool integrations - DVC & MLflow. We will create a repository on DAGsHub that comes automatically configured with a Git server, DVC storage, and MLflow tracking server. Next, we will use both of the tools as follows:
We will configure a remote machine (using Google Colab) to log an experiment to DAGsHub's remote MLflow tracking server and view it under the experiment tab.
We will configure the remote DVC storage with our local machine, version the project files using both Git & DVC, push and view them on DAGsHub.
The second part of this session is fully interactive! You can follow Nir's lead to create your very first DAGsHub repository, log an MLflow experiment, and push data files tracked by DVC.