Serve a Machine Learning Model as a Webservice

Serving a simple machine learning model as a webservice using flask and docker.

Getting Started

Use Model_training.ipynb to train a logistic regression model on the iris dataset and generate a pickled model file (iris_trained_model.pkl)
Use app.py to wrap the inference logic in a flask server to serve the model as a REST webservice:
- Execute the command python app.py to run the flask app.
- Go to the browser and hit the url 0.0.0.0:80 to get a message Hello World! displayed. NOTE: A permission error may be received at this point. In this case, change the port number to 5000 in app.run() command in app.py. (Port 80 is a privileged port, so change it to some port that isn't, eg: 5000)
- Next, run the below command in terminal to query the flask server to get a reply 2 for the model file provided in this repo:
```
   curl -X POST \
   0.0.0.0:80/predict \
   -H 'Content-Type: application/json' \
   -d '[5.9,3.0,5.1,1.8]'
```
Run docker build -t app-iris . to build the docker image using Dockerfile. (Pay attention to the period in the docker build command)
Run docker run -p 80:80 app-iris to run the docker container that got generated using the app-iris docker image. (This assumes that the port in app.py is set to 80)

Use the below command in terminal to query the flask server to get a reply 2 for the model file provided in this repo:

    curl -X POST \
    0.0.0.0:80/predict \
    -H 'Content-Type: application/json' \
    -d '[5.9,3.0,5.1,1.8]'

For details on floating the containerized app on AWS ec2 instance, see the blog.

See LICENSE for details.