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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

ML Model Deployment with Webhosting frameworks

asha
asha Last Updated : 23 Feb, 2021
5 min read

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

Introduction

With the motivation of award-winning from Analytics Vidhya Blogathon3 continuing this series for my previous blog Machine Learning Model  – server less Deployment.

In Machine Learning problems we usually start by defining start by defining the problem statement followed by data collection and preparation, understanding of the data, and model building but, in the end, we want our model to be available for the end-users so that they can make use of it.

ML Model Deployment with Webhosting frameworks

Figure: From my previous article

We have many blogs till now for ML model deployment but from case to case its different, few may choose Webhosting frameworks or server less compute or cloud platform-specific frameworks or Webhosting frameworks can deploy some PASS services like Heroku, or the same Webhosting framework can deploy using GCP compute engine, Azure VM services or AWS Ec2, etc…

There are many ways, from that, I have chosen one option to demonstrate in my blog i.e. ML model deployment using flask framework Heroku

Here, we cover the following topics :

  1. Major types of model deployments
  2. Save model and Create a web app using Flask and commit code in GitHub
  3. Create an account in Heroku
  4. Connect GitHub repository in Heroku
  5. Deploy the model

Let’s discuss this in detailed

1. Major types of model deployment

  •       Web Hosting Framework
  •       Server less Compute
  •       Cloud Platform specific frameworks

In my previous blog discussed “Server less Compute” now going to discuss “Machine Learning Model Deployment” using Web hosting Framework Flask

Let’s see a brief about flask :

The previous blog in Blogathon3 discussed server less compute  given link in the reference, now focusing on machine learning model deployment using Flask

Flask – a web services framework in python

Now, you might think what is a web service? Web service is a form of API only that assumes that an API is hosted over a server and can be consumed. Web API, Web Service Flask is a web service development framework in Python.

Flask Installation in python through pip

pip install flask

Flask framework comes with an inbuilt light-weighted web server that needs minimal configuration, and it can be controlled from our Python code.

Here, the code is used to create a simple Web-API which upon receiving a particular URL produces a specific output.

From flask import Flask

app = Flask(__name__)

@app.route(“/”)

def hello():

Return(“welcome to ML API”)

If__name__ == ‘__main__’:

App.run(debug=True)

We created an instance of the Flask class and passed in the “name” variable. This variable will be “main” if this file is being directly run through Python as a script. If you imported the file instead, the value of “name” would be the name of the file which you imported.

The hello() method is responsible for producing an output. Whenever your API is properly hit (or consumed). In this case, hitting a web-browser with localhost:5000/ will produce the intended output

Steps to be followed for Model deployment using Flask web framework

  • Serializing trained model parameters to disk

  • Serializing ‘scikit learn’ models using JSON, Pickel, and ‘joblib’

  • Including pre-processing steps as a part of model serialization

  • Building a text preprocessing and classification pipeline in scikit learn

  • Deploying the classification model to a Flask web application.

Serializing scikit-learn model – parameter serialization /deserialization

Choose json/ packle /joblib to serialize

JSON – require knowledge of what parameters to serialize because we use many models with many parameters

Pickle – Can work with any Python object not just with model estimator objects

Joblib – Setup pipeline object – all preprocessing steps and final state of the pipeline is our model that we apply to data. Sequence Apply to, series entire pipeline rather than serializing only parameters, pipeline used for test data also.

It can work great with a large multidimensional array, training and prediction data should be processed using the model parameters.

2. Save model and Create a web app using Flask and save code in Github

Steps to start model deployment

My GitHub repositions consist of “heroku_Flask_sal_deployment“, “DeployMLmodelUsing_Flask” from that I have chosen salary prediction based on experience, test_score, and interview_score regression model.

  1. Explaining about Data, code, and models

  2. Complete code details given in my GitHub README main branch  code can find in my master branch

This project has four major parts :

  1. model.py – This contains code for our Machine Learning model to predict employee salaries observed on training data in the ‘hiring.csv’ file.

  2. app.py – This contains Flask APIs that receive employee details through GUI or API calls, compute the precited value based on our model, and returns it.

  3. request.py – This uses the requests module to call APIs already defined in app.py and display the returned value.

  4. templates – This folder contains the HTML template to allow the user to enter employee detail and displays the predicted employee salary.

Run below coming in your command prompt

Using the Linear Regression algorithm create the model and serialize the model using pickle, reload and test the model using simple inputs.

python model.py 
<script src="https://gist.github.com/Asha-ai/8a94f5ef6a62faed06c95d9522f2e5ae.js"></script>

Finally, This would create a serialized version of our model into a file model .pkl

Start Flask API using the command

python app.py

<script src="https://gist.github.com/Asha-ai/81cd520b7f1716cd1db70c4a08899713.js"></script>

By default, the flask will run on port 5000.

Navigate to URL http://localhost:5000

Run the flask app:

flask Webhosting frameworks

3. Create an account in Heroku

What is Heroku :

Heroku is a cloud platform as a service supporting several programming languages. One of the first cloud platforms, it supports many programming languages, but now supports Ruby, Java, Node.js, Scala, Clojure, Python, PHP, and Go.

Create a Heroku account:

heruko webhosting framework

Connect GitHub repository:

 

Connect GitHub repository: webhosting framework

Choose master branch : Upload all code files in maser branch

Heroku generates URL: https://sal-prediction-flask.herokuapp.com/

Code References:

My GitHub Repository:

Readme in the main branch

Code files at the master branch

please make sure your code is in the master branch before connecting with HEROKU

Conclusion

Become a Full-Stack Data Scientist by learning various ML Model deployments and the reason behind my detailed explanation with screenshots is at the initial days I struggle a lot for learning ML Model deployment, So I decided my blogs should useful to data science enthusiasts.

Thanks for reading hope It helps you! and will meet you with my next blog: Deploy ML Model using “Cloud platform  Services“.

Please share your Comments and Suggestions!

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

asha
asha

Have 4+ years of experience in data science field, working in telecom domain With a strong foundation in Python, machine learning, deep learning, natural language processing (NLP), and artificial intelligence (AI).

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Muhammad Abdullah
Muhammad Abdullah

Can you please write a article on how to deploy ML models with FAST api on AWS cloud using Lambda functions or EC2 or Sage maker, any of them.

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