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Learn to design and apply AI model validation strategies that ensure reliable, fair, and robust AI systems in production. Topics covered include the core components of model validation, bias detection, stress testing, drift monitoring, stress testing, and tools like Scikit-Learn, and MLflow.

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Project Setup:

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You can use this repository's "Dev Container" to reproduce and run this project locally on your machine, regardless as to which operating system (MacOS, Windows, or Linux) you are on.

To set up the Dev Container, follow these steps:

  1. (If you haven't already) Install VS Code onto your machine

  2. (If you haven't already) Install Docker onto your machine

  3. On VS Code, add the following extensions (Click on the Extensions tab on the sidebar and search for the extensions in the marketplace):

    • Dev Containers

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    • Docker

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  4. With the above installed, you can now build the Dev Container following these steps:

    1. Ensure that your Docker daemon is running in the background

    2. Open your local repository/folder in VS Code

    3. Use VS Code's native "command palette" and execute the Dev Containers: Rebuild Container Without Cache command (Sometimes VS Code will even prompt you to do so when it recognizes the .devcontainer/ directory in the repo)

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      • This should start building your Dev Container in the background. You'll notice a window pop up (usually in the bottom right corner of VS Code) that you can click on to actively watch the build logs.
  5. Once the build completes, you're in! You can now open VS Code's native terminal to operate within the container and actively work on the codebase within the repository, just like you would otherwise normally do with VS Code (but now in an isolated and reproducible environment). 🙂

About

Learn to design and apply AI model validation strategies that ensure reliable, fair, and robust AI systems in production. Topics covered include the core components of model validation, bias detection, stress testing, drift monitoring, stress testing, and tools like Scikit-Learn, and MLflow.

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