Working in a command line environment is recommended for ease of use with git and dvc. If on Windows, WSL1 or 2 is recommended.
- Option 1: use the supplied file
environment.ymlto create a new environment with conda - Option 2: use the supplied file
requirements.txtto create a new environment with pip
- Create a directory for the project and initialize git.
- As you work on the code, continually commit changes. Trained models you want to use in production must be committed to GitHub.
- Connect your local git repo to GitHub.
- Setup GitHub Actions on your repo. You can use one of the pre-made GitHub Actions if at a minimum it runs pytest and flake8 on push and requires both to pass without error.
- Make sure you set up the GitHub Action to have the same version of Python as you used in development.
- Download census.csv and commit it to dvc.
- This data is messy, try to open it in pandas and see what you get.
- To clean it, use your favorite text editor to remove all spaces.
- Using the starter code, write a machine learning model that trains on the clean data and saves the model. Complete any function that has been started.
- Write unit tests for at least 3 functions in the model code.
- Write a function that outputs the performance of the model on slices of the data.
- Suggestion: for simplicity, the function can just output the performance on slices of just the categorical features.
- Write a model card using the provided template.
- Create a RESTful API using FastAPI this must implement:
- GET on the root giving a welcome message.
- POST that does model inference.