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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Which is Better for Machine Learning: Flask vs Django?

Gaurav Sharma
Gaurav Sharma Last Updated : 15 Mar, 2022
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction

We all know how popular the Python programming language is amongst Machine learning enthusiasts. So, once a machine learning model is ready, the next step is to deploy it to be used efficiently. But for deployment, there are various frameworks in Python that can be used. Flask vs Django is going to be an interesting comparison as both Python frameworks, and which one to choose for deployment is a good question. If you are also confused and stuck at the deployment stage, this article is for you. So, let’s deep dive and find out which is the best choice.

Python frameworks - ProgramingHub | Flask vs Django

                                                Image Source: https://programinghub.in

Choosing a Python Framework for model deployment depends on various criteria. We will go through each one by one. Firstly, let’s have a brief background to Flask and Django.

Flask

Flask is a micro web framework that is written in Python. Flask is very easy to learn, and also its implementation is straightforward. In just a few lines of code, you can get started with this. Flask is used in top tech companies also like: Netflix, Reddit, Mozilla, and so on.

Python + Flask - Part 3 | Flask vs Django

                                                                           Image source: https://dev.to

You can install this using pip: pip install flask.

With just this piece of code, you can get started:

from flask import Flask, render_template
app = Flask(__name__)@app.route('/')
def home():
    return "

Hey there!

”

if __name__ == ‘__main__’:
app.run(debug=True)

Features of Flask:

  • Built-in development server and fast debugger
  • Neat API
  • Flexible configurations
  • Integrated Unit testing support

Refer documentation here: https://flask.palletsprojects.com/en/2.0.x/quickstart/

Django

Django is also a web framework built on python. It’s open-source, accessible, and follows the MVC pattern(Model View Controller). Django provides excellent security features like CSRF, lightweight servers for dev and testing, etc. But since it has this MVC pattern, it confuses a lot of beginners. Instead, it is a perfect option for web development and deploying Machine learning models; many popular sites like Pinterest, Instagram, etc., are running on Django.

Building SaaS with Python and Django

                                            Image source: https://www.mattlayman.com/

Features of Django:

  • Secure framework
  • Versatile
  • Scalable
  • Maintainable
  • Portable

Refer documentation here: https://docs.djangoproject.com/en/4.0/

Head to head general comparison between Flask and Django:

Django Flask
It is a full-stack web framework and provides a lot of features. It is a lightweight framework with minimalistic features.
It comes with a ready-to-use admin framework that can be customized as well. No such feature here.
This framework ensures that developers use best practices because everything is template-based here. It is more open-ended, and developers don’t follow best practices here.
Django requires more lines of code. Flask required much fewer lines of code for the same task as Django.

 

Now, let’s discuss what should be selected from these two for your Machine Learning model deployment.

How big is your Machine Learning project?

Some of the Machine learning models are very simply trained; for them using Flask is a good choice because Django is very much featured bulky framework, and hence not recommended for use with such models. If you compare lines of code in the application written in Django as compared to the Flask, Django will always have more lines. Django also has a proper folder structure and many libraries, making it unsuitable for small and simple machine learning models.

So, Flask is sufficient for almost all the machine learning models. If you have minor HTML code(around 100 lines) and smaller CSS code, you need to write very little regulation in Flask for model deployment. Django can also be used, but it will be complex and more suitable for those developers who are advanced in Python. Because that would require a lot of documentation reading, understanding the structure, and various other things, whereas, with Flask, there is no such headache.

How familiar are you with Python?

Python is a straightforward and loved language, that comes with comprehensive and rich community support. Its syntax is concise, so a beginner does not face many issues while working with this. But instead of this, if you are not a master in Python, probably Flask is a good choice for you and will suit you more. But if you are a python expert, you might like Django more than the Flask.

Do you need Authorization and Authentication?

Django comes with an integrated package for handling authorization and authentication. Django allows you to configure Users, Groups, Password hashing systems, and many other configurations. You can find these functionalities in the Django.contrib.auth module of Django.

But when we talk about Flask, there comes no built-in authorization and authentication, and you require an extension in the Flask for these things. Some extensions which are used in Flask to get this functionality are Flask-login, Flask-WTF, etc.

So, you can see that in both Flask and Django, you can get authorization and authentication. Django provides you with in-built things, whereas in Flask, you have to manage through extensions.

Community support of both the frameworks

Every one of us, while developing something or writing programs, need help when we get stuck with bugs. Be it a pro or a newbie in coding; everyone needs community support at some point in time. And if you have solid and broad community support, life becomes much more accessible. So, when we discuss community support of both Flask vs Django, it is good, extensive, and knowledgeable. Django came into existence before Flask some five years ago. But instead of that, community support of both the frameworks is balanced and incredible for machine learning model deployment. You will most probably get answers to all your queries. So don’t worry about community support for both of them.

Do you need Form Handling?

Form creation and handling in both frameworks are comparable. Both provide client-side validation as well as server-side validation. They both also handle common security threats. But the design of forms from models is dealt with by Django’s ModelForm, whereas Flask does have a native form handling feature, and Flask has to rely on the Flask-WTF extension. Django also protects cross-site scripting, forgery, and SQL injection. So, both of these almost provide the required things.

Final Verdict

If you need to respond to an HTTP query or your model is small, and the codebase is light, go directly for a Flask-based application. But if you want to build something like next Facebook, Django will be a much better choice. If you have any confusion or doubt about which one to go for directly, go for Flask, it is straightforward, simple, and sure, it will get your work done. Django is also a fantastic package for building excellent applications, but it is confusing for beginners. For example, routing in the Flask is very easy, but that very same work in Django is a little bit complicated for beginners.

End Notes

So, in this article, we had a look at Flask vs Django and which one is better for deploying machine learning models. So, now you can take your decision quickly.

I hope you find this article helpful. You can connect with me on blog.

Thanks for reading my article on Flask vs Django :).

Happy coding!

Want to read more articles on Python’s frameworks? Then, head on to our blog now.

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blogathonmachine learningpython
Gaurav Sharma
Gaurav Sharma

Love Programming, Blog writing and Poetry

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Alex Ivanovs
Alex Ivanovs

Thanks, I like Django better.

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