Insurance Prediction Using Pycaret and Deployment in Google Cloud Platform

for creating this project i have refered this article https://towardsdatascience.com/deploy-machine-learning-model-on-google-kubernetes-engine-94daac85108b

This project gives us an understanding on:

1. Dockers or containers: Dockers are one of the tools which helps in environment standardizaion, that means regardless of which environmnet you run the application on docker provides equal response. Docker containers once created can be deployed anywhere.
2. kubernets: Kubernetes is a system for running and coordinating with docker containerized applications across a cluster of machines. It is a platform designed to completely manage the life cycle of containerized applications. Also, it helps to extend life cycle of the applications.

Difference between dockers and Kubernets, is that dockers is a tool that allows you to containerize applications. while kubernets are like the management systems which manages hundreds of dockers at a time.

Tool box for this tutorials are:

1. PyCaret: It is one of the technique where with low code, all type of analysis can be done. This library is capable of performing all data pre processing and machine learning tasks.
2. Flask: It is used to created web based applications. When we create a machine learning model we can create a server for it using flask api.
3. Google cloud platform: It is a cloud platform which can host your web page. To host your web page on this platform you need to create account on GCP.

Deploment of the code on Google cloud platform:

For DePloyment in GCP i followed the steps in the article provided above.