/Deploy-a-ML-Model-on-cloud-with-FastAPI

An end-to-end Machine Learning project to deploy final model on cloud platform with FAST API.

Primary LanguageJupyter NotebookOtherNOASSERTION

Deploye_FastAPI_WebApp_on_Cloud

Environment Set up

Download and install conda if you don’t have it already.

Run the following commends on terminal.

git clone https://github.com/Bhardwaj-Saurabh/Deploy-a-ML-Model-on-cloud-with-FastAPI.git

cd Deploy-a-ML-Model-on-cloud-with-FastAPI

conda create -n envn "python=3.8" 

conda activate envn

pip install -r requirements.txt

Running Instruction

  • To Train Model
python src/train_model.py
  • To Run Test
pytest src/test_pipeline.py
  • To Run the Web App Locally
uvicorn main:app

Web App deployed on Render

Deployed App can be access here

Data

  • Download census.csv from the data folder in the starter repository.
  • Information on the dataset can be found here.
  • 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.

Model

  • 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.

API Deployment to a Render Cloud Platform

  • Create a Free Cloud Application Platform account, such as Render.

  • Render Deployement

  • FAST Main Page Preview

  • FAST API Preview

  • FAST API Query Result