auto-trainer-core

Create a machine learning model training pipeline application using the 'auto-trainer-core' package. Your goal is to develop a user-friendly tool that allows users to easily train, evaluate, and deploy machine learning models without needing deep expertise in machine learning frameworks. This application will be particularly useful for data scientists and analysts who want to streamline their workflow and focus more on data analysis and less on model training logistics.

### Features:
1. **Model Selection**: Allow users to choose from a variety of pre-built models (e.g., linear regression, decision trees, neural networks).
2. **Data Preprocessing**: Implement basic preprocessing steps such as normalization, feature scaling, and handling missing values.
3. **Training & Evaluation**: Enable users to specify training parameters (like epochs, batch size) and evaluate models using standard metrics (accuracy, F1 score, etc.).
4. **Hyperparameter Tuning**: Integrate an automated hyperparameter tuning mechanism to optimize model performance.
5. **Model Deployment**: Provide an option to export trained models for deployment in a production environment.
6. **User Interface**: Develop a simple web-based UI where users can interact with the application, upload datasets, select models, configure settings, and view results.

### Utilizing 'auto-trainer-core':
- Use 'auto-trainer-core' to handle the underlying complexities of model training, evaluation, and optimization. Specifically, leverage its core objects and implementations to abstract away the need for manual configuration and setup of training pipelines.
- Explore the documentation of 'auto-trainer-core' to understand how it can simplify the process of integrating different types of models and preprocessing techniques into your application.
- Consider how you might extend or customize the functionalities provided by 'auto-trainer-core' to better suit the specific needs of your application.

Your task is to outline the architecture of this application, including key components like the backend logic and frontend interface. Also, provide sample code snippets demonstrating how 'auto-trainer-core' is integrated into the training and evaluation processes.