. . . . . . . . . . . . . "python" . . "3.9.15.final.0" . "@is_fairworkflow(label='My model training workflow')\ndef training_workflow(n_jobs: int):\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 loaded_model = save_model(\n model,\n \"models/my_model\",\n sample_data=data,\n scores=scores,\n hyper_params=hyper_params,\n )\n return loaded_model\n # Optionally you can save other files (dataframes, JSON objects) in the data/ folder\n" . . . "My model training workflow" . . . . . . . . . . . "2023-01-10T11:00:47.239704"^^ . "RSA" . "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAlKZel+uM9w+JiWuhs0NHtaXdRrGkGAjvnnLFQL2nmgZDGIjX+is2YJBd6R2kBkr67lYiUDWdtih0F0yqUlAW5GdYUihk1MBiWyLe4RNt7I5iC/5wwK/i5vV6/YKa6Zwf4oymwChpdXddlKc6d73StgFqOafQPeNeBUHTZbkmUKFAqGXf0nAclB23DEeN5qVl6XRa1qqtL91AKW+UxTvlZR3pFNFn8xKTfbOSgmuAkYSUwJrp84rNI92qXYtOJ12oH0oT/enzcThxcfPLomGc9BMd+QBCj08LdrtuAArHzcivMKDzreDCD+haZ/GdObRWHpQ5O4bEWIx3dZVQq7t0KQIDAQAB" . "iQD+mD3+UwR7sZ/wPN8IsJDpAHkspv1TQaYTACrG5BAKPl6HqD6fw31vw/u9sSSwCPBVO449lYSIXRn7CQIlwnunW0EpWnnjUHWXf3EbgLaQp1zigwlHxsg1Cmmz1gwVLcqx97BIEgJoswT/OswxcjHpZ5RCNwxEYb6aOX9GkVLPfXoAlLOnUwO8fw81YoM8qjT/2b4/F8Mmje1kbvmvb0zoWSiLjinBoExYJNTU91FXlSmdQIa9nuFNXA7wnbNwjZ+LYCPHcxRqPnjfrsh2V2o+lZ92ZZnAwzQxeHqZGmAHv/yigxFn33Ge2MaKy74LIazgelYMTHvOeMq1M2OtWw==" . . .