Train, compare and deploy AutoAI model

[rafalbigaj]

Using data set describing Marvel characters developed under #1, run AutoAI experiment and compare trained models with the model created on you own. Finally, deploy the model in Machine Learning service and use scoring endpoint to make predictions.

• Use Data Refinery to profile and visualize your data set describing Marvel characters developed under Marvel 'Bad characters' data set #1. If needed refine data and run job to generate final set.
• Create and run AutoAI experiment. From the best performers select 3 models and store them as notebooks in your project.
• Run stored notebooks and compare evaluation metrics for trained models. Compare them with metrics produced in AutoAI experiment. Why are they slightly different?
• Try to modify the training pipeline or propose you own to get better evaluation results.
• Select the best model (top performer) and store it in the deployment space. Deploy the model as a web service (scoring endpoint) using code in a nodebook.
• Optional: use (jupyter widgets)[https://ipywidgets.readthedocs.io/en/latest/#] in JupyterLab running on your local machine to prepare interactive form with controls allowing to change Marvel character features and visualize prediction results. If you will you can use any other tools to prepare interactive UI application leveraging the scoring endpoint. Rest API docs: https://cloud.ibm.com/apidocs/machine-learning#deployments-compute-predictions

Good luck!