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Make each dokcer image(preprocessing, training)\n    2. Make kubeflow pipeline(pipeline.py)\n    3. Start kubeflow\n    4. Upload pipeline and start experiment\n\n2. titanic\n  - Make kubeflow pipline for simple AWS example with titanic data\n  - The process of this kubeflow pipeline :\n    In preprocessing\n    1. Get data from AWS s3\n    2. Preprocessing data\n    3. Upload to AWS s3 after preprocessing data\n    Until here, doing in preprocessing\n\n    In train_model\n    4. Get preprocessing data from s3\n    5. Training model\n    6. Upload model to s3\n\n3. metrics_evaluation_and_check_condtion\n  - Make kubeflow evaluation and check condition\n  - Use iris data\n  - Evaluation\n    - Make metrics json\n    - Print metrics in kubeflow\n    - in 2_model_training/training_model.py\n  - Check condition\n    - Use dsl.condition\n    - in pipeline.py\n  - Result\n    - ![스크린샷 2020-07-23 오전 6 11 44](https://user-images.githubusercontent.com/24634054/88229282-72758600-ccab-11ea-8a4e-24bdb2a3ab27.png)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flsjsj92%2Fkubeflow_example","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flsjsj92%2Fkubeflow_example","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flsjsj92%2Fkubeflow_example/lists"}