Serve a Machine Learning Model as a Webservice

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

Getting Started

• 1.) Use Model_training.ipynb to train a model on the iris dataset and generate a pickled model file (iris_trained_model.pkl)
• 3.) Use app.py to wrap the inference logic in a flask server to serve the model as a REST webservice:
• 4.) Execute the command python app.py to run the flask app.
• 5.) Go to the browser and hit the url 0.0.0.0:5000 to get a website displayed to enter our observations.
• 6.) To deploy the machine learning model on Heroku, create first a heroku account.
• 7.) Login to Heroku
• 8.) Create a heroku app and connect to the GIT repository
• 9.) Once successfuly build and deployed start the Heroku app and use it for creating predictions