Data science project also have a lifetime or stages: step1:Understanding the problem step2:Data acquisition step3:Explore the data and data cleaning step4:EDA step5:Model making step6:Deployment of the model step7:Improvement of model
Deploying a model is the most crucial task in the data science project.
Having the knowledge and ability to deploy your machine learning model is an absolute necessity. Whether you’re building a model or generating reports, you need this skill. It takes that model that you poured your blood, sweat, and tears into and turns it into something that absolutely anyone can play with and admire. We will be seeing how to deploy the model using flask framework. We will first create the app and then we will deploy it using Baas or Paas platforms.
Paas:Platform as a service.PaaS is primarily a development and deployment platform that is responsible for executing the code and managing the application runtime.eg: AWS,Heroku,DigitalOcean
Baas:BaaS is not a container like PaaS. BaaS abstracts various services that the developers are expected to build and host by themselves. eg:creating the database and then contain the rest API and create the endpoints itself.