Build Machine Learning Pipeline in Python and Deploy on Cloud easily
In this tutorial we will build machine learning pipeline from scratch in Python using PyCaret library and a front-end web app using Flask framework and deploy it on Heroku cloud in few simple steps. You will learn:
- What is a deployment and why we deploy ML pipelines.
- Develop a machine learning pipeline using PyCaret.
- Build a simple front-end web app using Flask framework.
- Deploy ML pipeline with front-end web app on Heroku Cloud.
Tools we will use in this tutorial
PyCaret is an open source, low-code machine learning library in Python to train and deploy machine learning pipelines and models in production. PyCaret can be installed easily using pip.
# install pycaret
pip install pycaret
Flask is a framework that is used for building web applications in Python. A web application can be a commercial website, a blog, e-commerce system, or an application that generates predictions from data provided in real-time using trained models. If you don’t have Flask installed, you can use pip to install it.
# install flask
pip install flask
Heroku is a platform as a service (PaaS) that enables the deployment of web apps based on a managed container system, with integrated data services and a powerful ecosystem. In simple words, this will allow you to take the application from your local machine to the cloud so that anybody can access it using a Web URL.