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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

How to Develop Serverless Code Using Azure Functions?

chaitanya
chaitanya Last Updated : 30 Jan, 2023
6 min read

Introduction

Azure Functions is a serverless computing service provided by Azure that provides users a platform to write code without having to provision or manage infrastructure in response to a variety of events. Whether we are analyzing IoT data streams, managing scheduled events, processing document uploads, responding to database changes, etc. Azure functions allow developers to run a piece of code without having to provision or manage infrastructure in response to various events.

Scalability is built into Azure functions. When demand for execution increases, more resources are automatically allocated to the service, and when demand decreases, all extra resources and application instances are automatically decommissioned.

Azure Functions

Source: jiadongchen.com

 Learning Objectives:

In this article, we will

  1. Understand the basics of azure functions and how they are scaled and deployed.
  2. Learn about azure function features such as the pay-per-use pricing model, scalability, choice of languages, etc.
  3. Learn about hosting plans available for azure functions.
  4. Develop an azure function using C#, which will delete all the files from blob storage at 9:30 AM every day using Timer Trigger.

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

Table of Contents

  1. What are Azure Functions?
  2. Features of Azure Functions
  3. Triggers and Binding in Azure Functions
  4. Hosting Plans for Azure Functions
  5. Steps to Develop Azure Functions for a Scenario
  6. Conclusion

What are Azure Functions?

Azure Function is a trigger-based service that runs a piece of code or a script. As a serverless computing service, with the help of Azure Functions, users can run code without having to provision or manage infrastructure in response to various events.

Azure Functions are tremendous for stateless applications/solutions. Azure Functions provides users with a great choice of programming languages, such as Node.js, C#, Python, PHP, Java, etc., to write their code without worrying about any infrastructure configurations. Azure Functions automatically scale up and down based on the executions.

Azure Functions

Source:learn.microsoft.com

A function app consists of one or more functions that are scaled, managed, and deployed together. All functions in the same function app must have the same programming language, runtime version, pricing plan, and deployment approach.

Features of Azure Functions

• Serverless Applications: Azure Function allows users to write the code without having to provision or manage infrastructure in response to various events.

• Choice of Languages: Azure Function gives a variety of programming languages: C#, Java, JavaScript, Python, and PowerShell choice to the users.

• Pay-per-Use Pricing Model: Azure Function allows users to pay based on the consumption of the Azure Function.

• Scalable and Easily Upgradable: Azure Functions automatically scale up and down based on the executions.

Azure Functions

Source:learn.microsoft.com

• Easier to Write and Deploy: Users can easily write and deploy Azure Function using Azure portal, Visual Studio, or Visual Studio Code.

• Easily Connect to Other Services: With the help of triggers and bindings, Azure Function can easily connect to other resources or services like Logic Apps, Cosmos DB, Azure SQL, Blob Storage, Event Hub, etc.

Triggers and Binding in Azure Functions

Triggers are what cause a function to run. For example, A timer-triggered Azure Function runs based on the time of the timer object passed in the function.

Azure Functions

Source:www.serverless360.com

Binding provides a way of connecting another resource or service to the function.

Below are the two types of bindings:

1. Input Binding: An input binding is the data your function receives in the parameters. For example, Azure SQL input binding retrieves data from the Azure SQL database and passes it to the function input parameter.

2. Output Binding: An output binding is data sent by the function’s return value. For example, Azure SQL output binding lets you insert data into Azure SQL using Azure Function.

Hosting Plans for Azure Functions

Azure Function hosting plan decides how the function app is scaled, the resources available to the function app, etc.

microsoft azure

Source: hub.packtpub.com

Below are the hosting plans available:

1. Consumption Plan: It is the default hosting plan for Azure Function. Here, the function app is scaled automatically based on the load. Users have to pay only based on the consumption of the Azure Function.

2. Premium Plan: The premium plan provides more CPU or memory options.

3. Dedicated Plan: The dedicated plan controls scaling and computing.

Steps to Develop Azure Functions for a Scenario

Now, you are familiar with the concepts of Azure Function. Let’s see how we can build an Azure Function using C#, which will delete all the files from blob storage at 9:30 AM every day using Timer Trigger.

Follow the below steps:

1. Sign in to your Azure Account. Create a resource (+). Select Storage Account.

Azure Functions

Azure Functions

2. Enter the information in the requisite fields on each tab of the Storage Account.

Azure Functions

3. Click on Review + Create.

microsoft azure

4. After creating the Azure Storage Account, click on + Container. Provide demo-delete in the name and select Container in the public access level. Click Create.

microsoft azure

5. Now, in the demo-delete, upload one file. The file has been uploaded successfully.

microsoft azure

6. Create an Azure Function App deptCreate with a Function named “ProcessBlobData.”

microsoft azure

7. A timer-triggered Azure Function runs based on the time the timer object passes in the function. Now, open Visual Studio. Click File>New>Project> Search for Azure functions in the template. Click Azure Functions> Next. Provide the Project name. Click Next> Timer Tigger. Keep the default selected values and click Create.

creating new project

creating new project

8. After the project gets created, open the Function1.cs file and paste the below code snippet into the file:

            log.LogInformation($"Timer trigger function executed at: {DateTime.Now}");
            string blobConn = Environment.GetEnvironmentVariable("BlobConn");
            BlobContainerClient blobContainerClient = new BlobContainerClient(blobConn, "demo-delete");
            var blobs = blobContainerClient.GetBlobs();
            foreach (BlobItem blobItem in blobs)
            {
                blobContainerClient.DeleteBlobIfExistsAsync(blobItem.Name);
                log.LogInformation($"Blob Name {blobItem.Name} is deleted successfully.");
            }

code

9. Install the Nuget Package: Azure.Storage.Blobs

code

10. In the local.settings.json, paste the connection string for Azure Storage Account in the BlobConn.

code

11. Right Click on Solution > Publish > Azure Function App (Windows)> Next.

code

12. Now, select the deptCreate function app and click Finish.

azure functions

13. Run the function from Azure. We see that all the files got deleted.

azure functions

Conclusion

This article shows how users can write the code without having to provision or manage infrastructure using Azure Functions. We have seen how one can easily write and deploy Azure Function using Azure portal, Visual Studio, or Visual Studio Code. Azure Function can easily connect to other resources or services like Logic Apps, Cosmos DB, Azure SQL, Blob Storage, Event Hub, etc., with the help of triggers and bindings. Apart from this, we have seen how they automatically scale up and down based on the executions. Below are some major takeaways about Azure Functions:

1. We have learned about the function app and its features.

2. We deeply understood that we could connect other Azure services to Azure Functions using triggers and bindings.

3. We have seen how we can manage scaling, CPU, and memory options using Azure Function Hosting Plans.

4. Apart from this, we have learned to develop an Azure Function using C#, which will delete all the files from blob storage at 9:30 AM daily using Timer Trigger.

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

chaitanya
chaitanya

Full Stack developer with 2.6 years of experience in developing applications using
Azure, SQL Server & Power BI. I love to read and write blogs. I am always willing to learn new technologies, easily adaptable to changes, and have a good time
management, problem-solving, logical and communication skills

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