. . . . . . . . . "python" . . "3.9.15.final.0" . "@is_fairworkflow(label='My model training workflow')\ndef training_workflow(n_jobs):\n\n # Define models hyper params\n hyper_params = {\n 'n_jobs': n_jobs,\n 'random_state': 42\n }\n\n data = load_data()\n y = load_data_y()\n\n # Train model (here we use a stub dataset just to make the example clear)\n model = fit_classifier(hyper_params, data, y)\n\n # Evaluate the model using your own metrics\n scores = evaluate(model)\n\n # Save the model generated to the models/ folder\n path = save(\n model,\n \"models/my_model\",\n sample_data=data,\n scores=scores,\n hyper_params=hyper_params,\n )\n return path\n # Optionally you can save other files (dataframes, JSON objects) in the data/ folder\n" . . . "My model training workflow" . . . . . . . . . . . . . . . "2022-12-21T14:46:41.367837"^^ . "RSA" . "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAlKZel+uM9w+JiWuhs0NHtaXdRrGkGAjvnnLFQL2nmgZDGIjX+is2YJBd6R2kBkr67lYiUDWdtih0F0yqUlAW5GdYUihk1MBiWyLe4RNt7I5iC/5wwK/i5vV6/YKa6Zwf4oymwChpdXddlKc6d73StgFqOafQPeNeBUHTZbkmUKFAqGXf0nAclB23DEeN5qVl6XRa1qqtL91AKW+UxTvlZR3pFNFn8xKTfbOSgmuAkYSUwJrp84rNI92qXYtOJ12oH0oT/enzcThxcfPLomGc9BMd+QBCj08LdrtuAArHzcivMKDzreDCD+haZ/GdObRWHpQ5O4bEWIx3dZVQq7t0KQIDAQAB" . "FzZrtHcrU+IbU+h0WceEivucZa/3gucL9kPXjKMgY73DgPC6pBm3mx4MG4mQFhOPRYL/iquVoT/5+LfTKhm96vstMdmMHoTw1mlzi/yafDTb7AVLoAMsERnNHA01iIn1nbBsxeLTZblZNvu26YWFK+XwxIz6bwDx2b5hU+6bahZnz9cgDup+DsvWSTchyyOulicFdlstdTtCMr77eGOxAjfVZk1ZJvsIn6Sfm1wLphPjIhUSnnz3dWrDxgGwjGhRTazKRFW5IXf37DW4+hCuNHzbO5jkRcJN9NCJbT6xpE517npjZZZynTS33TWr6F7FyODG9WVc84ecq7eXtV0tTA==" . . .