1
Install MLRun Client
Install the MLRun client package via pip and configure it for either a local or Kubernetes backend.
2
Import Function
Import a function from the Function Hub, such as 'hub://auto_trainer', to perform model training.
3
Create Project and Run Function
Create a project and run the imported function with inputs like datasets and parameters (e.g., model class, train-test split).
4
View Results
Use the MLRun UI to view results, metrics, and artifacts from your runs.
5
Deploy Serving Function
Deploy serving functions for real-time inference using the 'deploy_function()' method integrated with Nuclio.