Open AccessReview on Frameworks Used for Deployment of Machine Learning Model
Author(s) -
Himangi Dani
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.40222
Subject(s) - software deployment , computer science , artificial intelligence , variety (cybernetics) , machine learning , web service , field (mathematics) , service (business) , software engineering , world wide web , mathematics , economy , pure mathematics , economics
According to the current scenario, the use of machine learning is increasing in a variety of web applications and services. A good visual experience, fast performance, and easy to use framework is critical for developing and deploying your model. Working on a machine learning model is one thing but deploying a machine learning model to production can be another. Creating a Machine Learning model is one thing but deploying the model in real-time is the real challenge. For that purpose, many different technologies are available in the field. The simplest way to deploy a machine learning model is to create a web service or application. In this paper, we will discuss different frameworks for the deployment of the machine learning model on web applications or services. In this paper we will discuss Flask framework, Streamlit framework, Django Framework. Keywords: Flask, Streamlite, Django Framework, Model deployment, Web Framework