arize

Create a monitoring dashboard application for machine learning models using the 'arize' Python package. This application will allow users to monitor the performance of their models in real-time by integrating seamlessly with Arize AI's APIs. The app should have the following functionalities:

1. **Model Registration**: Allow users to register new machine learning models with unique identifiers, model names, and descriptions.
2. **Data Ingestion**: Implement a feature where users can upload batches of predictions from their models along with the corresponding ground truth data. This data will be sent to Arize for analysis.
3. **Performance Visualization**: Develop a dashboard that displays key performance metrics such as accuracy, precision, recall, F1 score, etc., derived from the data analyzed by Arize. The dashboard should also include visualizations like confusion matrices and ROC curves.
4. **Alert System**: Set up an alert system that notifies users via email or SMS if any of the performance metrics drop below predefined thresholds.
5. **Custom Metrics**: Provide an option for users to define custom metrics that they want to track alongside the standard ones provided by Arize.
6. **Historical Data Analysis**: Enable users to view historical performance data over time to identify trends and patterns.
7. **User Authentication**: Ensure that each user has their own account and can only access and manage their registered models.

To achieve these functionalities, you will need to utilize the 'arize' package's core features such as registering models, ingesting predictions, retrieving performance metrics, and handling alerts. Make sure to document your code well and provide clear instructions on how to set up and run the application.