ML Pipeline

AI Project Template

The goal of this project is to create a template for AI projects. The template is designed to be as simple as possible, while still being able to handle a wide variety of AI problems. The template is designed to be used with the PyTorch (opens in a new tab) library, but it can be used with any library.

The Importance of Abstraction

When creating a new AI project, you should be able to focus on the AI problem you are trying to solve, not the details of how to implement it. Luckily, most AI problems can be broken down into a few simple steps:

  • Data Loading: Load the data from a file or database.
  • Data Preprocessing: Preprocess the data to make it easier to work with.
  • Model Training: Train the model to solve the problem.
  • Model Evaluation: Evaluate the model to see how well it performs.
  • Model Deployment: Deploy the model to a production environment.


  • Standardizing Project Structures: Learn about the importance of a standardized project structure to ensure consistency, collaboration ease, and maintenance across different projects.
  • Reusable Components: Create templates for data preprocessing, model training, evaluation, and logging to streamline future project setups.
  • Version Control Integration: Integrate version control systems like Git to manage changes and collaborate effectively with team members.
  • Documentation Standards: Emphasize the importance of comprehensive documentation, including code comments, README files, and usage guides.
  • Practical Lesson: Design a project template that can be reused for different ML projects, incorporating best practices for structure and documentation.