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Reading list

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

Deploying a Flask App on AWS Elastic Beanstalk

Nibedita Dutta
Nibedita Dutta Last Updated : 02 Nov, 2021
5 min read
This article was published as a part of the Data Science Blogathon.

 

 

aws beanstalk

Image 1- https://static7.depositphotos.com/1020804/754/i/600/depositphotos_7541455-stock-photo-green-beans.jpg

Whether you are an experienced or an aspiring data scientist, you must have worked on machine learning model development comprising of data cleaning, wrangling, comparing different ML models, training the models on Python Notebooks like Jupyter. All the above steps are of course undoable without the presence of such notebooks. However, these notebooks are limited for use by the developers as they cannot be directly utilized for an application by any non-technical user.

Moving from Notebooks to Products

If you use any website for checking the weather forecasts of your city, the forecasts that you see are coming from a model that might have got developed in some notebook but the users are using the website for the forecasts.

As you must have guessed, there are therefore ways to convert the notebooks to a form that can be integrated into a product that can be accessed and utilized by any person – technical or non-technical users. This process is called ‘model deployment’.

model deployment process

Image 2 – https://data.solita.fi/wp-content/uploads/2020/05/anniina_kuva01artboard-2-1.png

What’s in this Article for You?

  1. ✔️ Get a basic understanding of AWS ElasticBeanstalk – what it does, when should you use it
  2. ✔️ Get to know how to build a basic Flask App
  3. ✔️ Learn stepwise how to deploy the app on AWS ElasticBeanstalk
  4. ✔️ Understand when you should use AWS ElasticBeanstalk for deployment over other AWS services

Icebreaking with AWS Elastic Beanstalk:

aws beanstalk

AWS Elastic Beanstalk is a compute service that allows you to upload code of your web application along with environment configurations – based on which Elastic Beanstalk automatically provisions and deploys the necessary resources required within AWS to make the web app operational. These resources can include other AWS services and features like EC2 instances, elastic load balancing, autoscaling, etc.

As a data science professional, if you are not familiar with the nitty-gritty of model deployment, it would be ideal for you to use AWS Elastic Beanstalk to deploy your ML model as it automates and simplifies the whole process of deployment.

How much does it cost to use AWS Elastic Beanstalk? 

AWS Elastic Beanstalk as a service is free to use. However, the resources that are utilized to create the application like EC2 instances come at a charge as per the standard pricing policy at the time of deployment.

Benefits of AWS Elastic Beanstalk

  1. ⭐   AWS EBS offers a simple and quick way to deploy web applications.
  2. ⭐   With EBS, you can focus on your application code rather than provisioning and configuring AWS resources.
  3. ⭐   Auto-scaling settings on EBS helps automatically scale the application.
  4. ⭐   One has control over all the AWS resources like EC2 that are powering the app.
  5. ⭐   AWS EBS provides a cost-effective price in which one has to pay for what one uses and there are no hidden costs.
  6. ⭐   AWS EBS supports Java, .NET, PHP, Node.js, Python, Ruby, Go, and Docker web applications.
  7. ⭐   Access to monitoring metrics like CPU utilization, request count, and latency.
model deployment

 

Steps to Follow for Deployment👣 

Things you need:

  1. ☝  AWS account
  2. ☝  Knowledge of Python (Jupyter Notebooks)

✍ Building a Flask API

Flask is a web framework that can be used to build web apps with Python.

We can install ‘Flask’ using the following line of code :

pip install flask

Post installing, you could import the library and then create an application object. We add a route (“/”) to display the ‘Hello World! ’ message. A route enables mapping a web URL to a Python function. Whatever the function returns is shown to the user on the web URL.

from flask import Flask
application = Flask(__name__)
@app.route(“/”)
def hello():
    return “Hello World!”
if __name__ == “__main__”:
       app.run()

Write the above lines of code in a .py file and name it as ‘application.py’.

✍ Creating a Requirements.txt file

We will need a requirements.txt file that lists all the libraries that we have used in our ‘application.py’ file. For our case, this consists of only the following line:

Flask==1.0.2
flask

After we have created this file, we can zip these two files that can be later used while creating the application.

✍ Creating an application on  AWS Elastic Beanstalk

Sign in to AWS Console and search for AWS ElasticBeanstalk. Click on ‘Create Application. We can give our application a name. For the platform, we choose Python 3.8 and for the application code, we upload the .zip file that we created before.

aws elastic beanstalk

 

aws elastic beanstalk

✍ Creating an Environment

Once we have uploaded the source code of our application, we can see the environment of our application getting created and finally the application getting created with a web URL assigned to the application.

Elastic Beanstalk

What else do you get?

  • 👉 Dashboard for checking the health status of your app and recent events.
  • 👉 Configuration board using which you can manage the entire environment.
  • 👉 Logs – the logs from your app that enables tracking what went wrong in case your deployment fails
  • 👉 Monitoring board – for checking some basic metrics like CPU utilization and network traffic.
  • 👉 Alarms based on monitoring metrics – Warning email could be set up which you can receive when for instance CPU is utilized over 80% for a specific period of time.
analytics

When to deploy AWS Elastic Beanstalk over EC2?

EC2 is Amazon’s service that can be utilized for creating a server (or instances) in the AWS cloud. On the other hand, Elastic Beanstalk is one layer of abstraction away from the EC2 layer. Elastic Beanstalk sets up an “environment” for us that can contain multiple EC2 instances, an optional database, as well as a few other AWS components such as elastic load balancing, autoscaling, etc. Elastic Beanstalk manages these items for us whenever we want to update our application.

If you are trying to deploy a simple app in a short period of time(like an app that just displays a list of things), you can go for Elastic Beanstalk.

⚠️⚠️ However, there are some situations when you would like to prefer EC2 over AWS Elastic Beanstalk. If you have developed an app that has a lot of things going around in it like multiple file uploads, simultaneous calculations, continuous delivery of notifications to users, etc., it would be a better choice to leverage EC2.

About Author

Nibedita Dutta

Nibedita completed her master’s in Chemical Engineering from IIT Kharagpur in 2014 and is currently working as a Senior Consultant in an analytics firm. In her current capacity, she works on building AI/ML-based solutions for clients from an array of industries.

Image Sources:

1.https://static7.depositphotos.com/1020804/754/i/600/depositphotos_7541455-stock-photo-green-beans.jpg

2.https://data.solita.fi/wp-content/uploads/2020/05/anniina_kuva01artboard-2-1.png

3.https://blog.eventzilla.net/event-resources/10-icebreaker-ideas-for-networking-events/

4.https://www.kindpng.com/imgv/hRxxTh_qlik-dashboard-icon-hd-png-download/

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Nibedita Dutta
Nibedita Dutta

Nibedita completed her master’s in Chemical Engineering from IIT Kharagpur in 2014 and is currently working as a Senior Data Scientist. In her current capacity, she works on building intelligent ML-based solutions to improve business processes.

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