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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

Setting Up CI/CD Using GitHub Actions

aditya8495d
aditya8495d Last Updated : 11 Oct, 2022
6 min read

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

Introduction

CI/CD refers to the process in which your development team involves frequent code changes pushed in the main branch while ensuring that it does not impact any changes made by developers working parallelly. It provides smooth and convenient management.

We will be doing CI/CD with the help of GitHub actions. GitHub Actions make it easy to automate all your software workflows now with the help of CI/CD. Build, test, and deploy your code right from GitHub. It uses YAML syntax to define the events, jobs, and steps. These files are stored in GitHub workflows, and a series of commands are automatically triggered whenever a code is pushed.

CI/CD
Source – https://journeyofquality.com/2020/11/20/23-ci-cd-tools/

Various Stages of the CI/CD pipeline:

Build – We will compile the code in this stage.

Test – We will test the code in this stage. We can save both efforts as well as time can be saved by performing the techniques of automation.

Release – In this stage, we will release the application in our GitHub repository.

Deployment – We will deploy the application to the production environment.

Validation and compliance – Your organization’s needs determine the steps to validate a build.

What are GitHub Actions?

1. GitHub Actions help to automate tasks within the software development life cycle.

2. GitHub Actions are event-driven, i.e., we can run a series of commands after a specified event has occurred.

3. We can use GitHub Actions to automatically run your software testing scripts. An event automatically triggers the workflow, which contains a job. The job then uses steps to control the order in which actions are run. The workflow is an automated procedure that you add to your repository. Workflows are one or more jobs that can be scheduled or triggered by an event. We can use the workflow to build, test, package, release, or deploy a project on GitHub. A job is a set of steps that execute on the same runner. By default, a workflow with multiple jobs will run those jobs in parallel.

CI/CD
Source – https://docs.github.com/en/actions

 

Prerequisites or Requirements

1. A machine with 256 MB of RAM, although more than 2 GB is recommended

2. GitHub account and the project repository added to GitHub, which you want to keep track of.

3. Complete information about the frameworks used in the project(along with their versions).

CI/CD using GitHub Actions

This section will show how we can set up a CI/CD pipeline on our applications using GitHub. To get started, firstly, we have to create a new repository. Clone that repository on your machine. Now, open your command line tool that opens the cloned repository and push your application there. Once we have the application code in the repository, we can start creating workflows in GitHub actions. Workflow can vary on a requirement basis or from project to project.

Below are the detailed steps of the complete workflow:

1. Click on the Actions button available on the repository navbar.

CI/CD

2. Select the workflow you want to apply (GitHub will automatically recommend the workflows based on your code). For, e.g. for a react project, we will choose the node.js package.

GIT Hub actions

3. Now, you can see a workflow file is created, which is a YML file. You can make the pipeline by directly editing the file. We can include the dependencies, the job step that needs to be done, and the command so that it can now deploy if everything is successful.

CI/CD

Here is the link to my pipeline code. I have created steps to build, run, test, and deploy the code locally whenever a code is pushed on GitHub. As it is a react project, I have selected node.js workflow for the project.

4. After editing the XML file, click on start commit->commit.

5. You will see that your build has failed because we have not installed any runner for git actions. To resolve this, click on settings->Actions(On side Nav).

react application
Failed to Build because no runner

6. Scroll to the bottom and click on add runner.

self-hosted runners

7. Now, a series of commands appear to run the runner. Open your command prompt(so that we can run these commands) or any terminal where you are working and go to your project directory. And also, in GitHub, select the command according to your need.

CI/CD
All commands we need to run to set up a runner

8. Create an actions runner folder and cd into it in the terminal run. I was using the commands mkdir actions-runner and cd actions-runner.

9. Now download the latest runner package by running the command given in Github. You will see the following screen.

running all commands
Running All Commands

10. Next, configure the file by running the commands above. Use the token number provided in GitHub to connect to GitHub. We have completed the setup for the GitHub actions. We can see the following message

runner
The runner is now online

11. We have built out a runner, but on GitHub, you will see our runner is offline.

To make the runner online, run the last line given above, i.e. ./run.cmd on windows. And now we can see that the runner is idle(running or online).

react app
Runner status changed to idle

12. Now, we are ready to complete the CI/CD process. Go to actions->latest workflow->on the top right corner, click on run all jobs.

CI/CD process
Continuous Integration is getting Done.

Viewing progress on the build option on the right side will take some time.

CI/CD
We can see that our build is booming, and the app is automatically
deployed.

Please Note that Continuous delivery is automatically done if you have written your terminal commands (which we use to run the app locally) in your pipeline file. For example, if we have a react application in the pipeline file, we need to write run: npm start will deploy our app, and hence continuous delivery is performed.

Thus, With the help of GitHub actions, continuous Integration and delivery can be done quickly.

Conclusion

We have successfully integrated CI/CD on our basic web app using GitHub Actions.

I will briefly summarise all the points I have covered in this article.
1. Set Up Jobs: This step allows us to set up the required environment and packages.
2. Run action check: The AUTH token is persisted in the local git config. It enables your scripts to run authenticated git commands. We will remove the token during the post-job cleanup.
3. Run subosito/flutter-action: This action creates a flutter environment for active use. It works on Linux, Windows, and macOS.
4. Install Dependencies: We use Install dependencies to install dependencies for our project. Run flutter pub get to make sure the dependencies are up to date.
5. Analyze: Analyzes each commit or pull request by running all supported static code analysis tools for the languages found in your repository.
6. Format: It runs the Run flutter format command. Post-Run Actions/ setup java@v1 A simple GitHub action that enables running post-run steps once a workflow job has ended.
7. Complete Job: See Events that trigger workflows for a complete list of events that can be used to trigger workflows.

It is all for today. If you have any suggestions or doubts, please feel free to comment below. I will reply to your queries.

Thanks for reading, 🤩

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

blogathonCI/CD Pipelinedata science githubGithub
aditya8495d
aditya8495d

As a content writer with experience in data science and data engineering, I am skilled in creating clear, concise and engaging written materials for a variety of audiences. My technical knowledge in these fields allows me to accurately convey complex ideas and concepts in an easy-to-understand manner. In addition, I am constantly seeking to learn and stay up-to-date on the latest trends and developments in data science and data engineering.

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