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https://github.com/mikethwolff/ML-FastAPI
This repository lists one of my projects and findings as part of my Machine Learning DevOps Engineer Nanodegree.
https://github.com/mikethwolff/ML-FastAPI
continuous-deployment continuous-integration fastapi machine-learning-pipeline model-bias-analysis
Last synced: 2 months ago
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This repository lists one of my projects and findings as part of my Machine Learning DevOps Engineer Nanodegree.
- Host: GitHub
- URL: https://github.com/mikethwolff/ML-FastAPI
- Owner: mikethwolff
- Created: 2024-02-03T17:01:19.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-04-27T17:09:11.000Z (8 months ago)
- Last Synced: 2024-07-29T17:44:03.989Z (5 months ago)
- Topics: continuous-deployment, continuous-integration, fastapi, machine-learning-pipeline, model-bias-analysis
- Language: Jupyter Notebook
- Homepage: https://www.udacity.com/certificate/e/d6b5b0e2-4d89-11ee-b9c4-93e0b6acb2ad
- Size: 1.89 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Machine Learning Model Deployment with FastAPI and Heroku
## Census project starter kit
Census project: Predict whether income exceeds $50K/yr based on census data. Also known as Adult dataset.
Data has been downloaded from the [Udacity nd0821-c3 project starter kit](https://github.com/udacity/nd0821-c3-starter-code/blob/master/starter/data/census.csv)
The UC Irvine Machine Learning Repository is where you can find information on the [original dataset](https://archive.ics.uci.edu/dataset/20/census+income)
## Environment
- Create your conda environment:
```
$ conda create --name --file requirements.txt
$ conda env create --file conda.yaml
$ conda activate
```
## Data cleaning:
Data cleaning can be performed by using the Jupyter notebook ["Census_Clean_Data.ipynb"](Census_Clean_Data.ipynb).
The notebook also provides a good overview of the data.
Data cleaning will also be provided via Python file: ["/ml/data_cleaning.py"](/ml/data_cleaning.py)
## Sanity check:
```
$ python -m tests.sanitycheck
```
Answer path question with "tests.api_tests.py" as test file for a check of functionality to meet course specifications
## Training the model:
```
$ python -m ml.train_model
```
After the model has been trained successfully, the following files will be saved:
Metrics will be written to ["/artifacts/slice_output.txt"](/artifacts/slice_output.txt)
Model will be saved to file ["/artifacts/model.joblib"](/artifacts/model.joblib)
Encoder will be saved to ["/artifacts/encoder.joblib"](/artifacts/encoder.joblib)
Label binarizer will be saved to ["/artifacts/lb.joblib"](/artifacts/lb.joblib)
The output will be shown on screen and also be saved in ["/logs/census.log"](/logs/census.log)
## Census API tests:
Start the uvicorn server with:
```
$ uvicorn main:app --reload
```
The server is then accessible via: ["http://127.0.0.1:8000"](http://127.0.0.1:8000)
Documents can be found here: ["http://127.0.0.1:8000/docs"](http://127.0.0.1:8000/docss)
FastAPI tests can be performed by using the Jupyter notebook ["Census_Tests_API.ipynb"](Census_Tests_API.ipynb).
## Pytest:
Pytest will run all tests in the tests folder and can be executed via:
```
$ pytest -vv
```
## Model card
Find detailed information in the ["model_card.md"](model_card.md)
## GithubActions
If changes have been made, github actions is called.
## Continuous Deployment to Heroku
The Heroku app can be tested by using the Jupyter notebook ["Census_Test_Heroku.ipynb"](Census_Test_Heroku.ipynb).
The app deployed on Heroku can be accessed at: ["https://census-salaries-d3e2956470bf.herokuapp.com/"](https://census-salaries-d3e2956470bf.herokuapp.com/)