bjornorri / moonraker

A sample project for an ML engineering interview.

Community Questions

Introduction

This is a demo ML classifier that runs in a Docker container and exposes an API enpoint for predictions.

API in Docker

Getting Started

1. Clone the repository.

git clone https://github.com/bjornorri/moonraker.git

2. Navigate to the project directory.

cd moonraker

3. Build the docker container image.

docker build -t moonraker .

4. Start the docker container .

docker run moonraker

5. Locate the Docker container's IP address.

docker ps # Get container id
docker inspect CONTAINER_ID # Get IP address

6. Access the prediction API at http://IP_ADDRESS/predict?VisitsLastYear=123&QuestionTextLength=456. Example:

curl "http://172.17.0.3/predict?VisitsLastYear=123&QuestionTextLength=456"

Request and response format

Provide the following parameters as query parameters. These are used as input features for the model.

Name	Type
VisitsLastYear	integer
QuestionTextLength	integer

Example response:

{ "prediction": false }

Jupyter Notebook

To see how the model was created, view the provided Jupyter notebook.

Getting Started

1. Clone the repository.

git clone https://github.com/bjornorri/moonraker.git

2. Navigate to the project directory.

cd moonraker

3. (Optional) Create and activate a virtual environment.

python3 -m venv venv
source venv/bin/activate

4. Install dependencies.

pip install -r requirements.txt

5. Run the Jupyter server. This should launch your browser.

jupyter notebook

6. Open community_questions.ipynb.

Advanced model

If I create a more advanced model (optional task), I will do it on another branch in this repository.