Best Python Libraries For Deploying Machine Learning Models As Web App
Useful Libraries With Source Code and Working Examples
A Data Science Project Lifecycle Includes multiple steps from collecting data using web scraping or APIs to performing feature engineering to model training and testing models. Out of all the steps, there is only one step that includes the user in it the — Model deployment. It includes deploying the saved model as a web app, GUI, or an os based application. Deploying models as a web app is one of the most famous and used methods that most developers used to test their models on real-life data. In this blog, I will be sharing a list of libraries that help turn a model into a web app, also the process they need to follow with an example. We will deploy the same model with different libraries so that you can decide which one is best for you.
We will be creating a simple NLP-based sentiment analysis model to predict whether a certain review is positive or negative. The dataset for the same can be found on my Github Repository.
I will be using Jupyter notebook for training the model, and Visual Studio Code for writing the deployment Code. Everything is done inside a virtual anaconda environment.
You can find the dataset used in the article on Kaggle.
