MLE Interview:

Setup

1. Install Visual Studio Code. Also the following plugins:
   • Dev Containers
2. Clone this repo
3. Open the repository in VSCode (code .)
4. VSCode will prompt you to install recommended extensions. Select Yes
   • If you missed this step, you can install recommended extensions using the extensions sidebar (⇧⌘X)
5. VSCode will prompt you to reopen the workspace in a Dev Container. Select Yes.
   • If you missed this step, open the command prompt palette (⇧⌘P) and run the command Remote-Containers: Rebuild and Reopen in Container

Make Commands

Before you start, ensure you have all the necessary dependencies installed:

make setup

This command will install the Python dependencies listed in requirements.txt.

Serve API

To start the Flask API, use the following command:

make serve-api

This command launches the Flask application defined in api/app on port 5005, accessible via 0.0.0.0. The --debug flag is included to enable debug mode, which provides useful debugging information in case of errors and automatically reloads the server on code changes.

Serve Model

If you're using a TensorFlow model served via Docker, you can start the TensorFlow serving using:

make serve-model

This command uses Docker Compose to spin up a container defined in the Docker configuration, specifically targeting the TensorFlow serving service (tf-serving). Ensure Docker is installed and running on your system before executing this command.

Train

To train the model, execute:

make train

This command runs the main training script located at model.main.

Test

Finally, to run the tests for your project, use:

make test

This command runs all the tests in the ./tests directory using pytest.

API

Route: GET: http://localhost:5005/interests/:userHandle

API Response:

{
    "user_handle": "e337a675-46f5-437e-aa72-5d43643b5461",
    "interests": [
        {
            "id": "29e020bb-7a02-41db-b21f-527a8ef4dfdf",
            "label": "Accounting technician"
        },
        {
            "id": "012abcd1-3a6a-4803-a47e-42f46b402024",
            "label": "Field seismologist"
        },
        {
            "id": "a1b028dd-8464-4c63-85e8-ae29ea184fc7",
            "label": "Designer, industrial/product"
        },
        {
            "id": "686af7e4-6d58-4148-b227-3bf65ff10273",
            "label": "Materials engineer"
        },
        {
            "id": "8d1526b9-a7bf-4972-be43-7b912f149667",
            "label": "Fashion designer"
        },
        {
            "id": "d83a55a3-0143-4318-91c1-88f44ad59390",
            "label": "Journalist, newspaper"
        },
        {
            "id": "cda4441f-dba6-495c-9e2e-7429bd5e0465",
            "label": "Therapist, music"
        },
        {
            "id": "e9b23ad4-753b-4331-9cfa-525965fcf281",
            "label": "Secretary, company"
        },
        {
            "id": "ca4b03b2-0ae2-440a-afc8-5469510b19cb",
            "label": "Commissioning editor"
        },
        {
            "id": "fd3c41b8-8c15-47e2-a80d-cf3683b2d0da",
            "label": "Copywriter, advertising"
        }
    ],
    "name": "Kisiza",
    "type": "B2B"
}