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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

What are Microsoft Azure Functions?

Yana Khare
Yana Khare Last Updated : 31 Oct, 2023
6 min read

Introduction

Microsoft Azure’s wide range of services has allowed it to carve itself a niche. Azure Functions is a dynamic and effective serverless computing solution that stands out. This article takes a closer look at Azure Functions, including how they function, who may use them, and even a hands-on tour of a small project highlighting their practical uses.

Microsoft Azure
Source: LMS Group

Table of contents

What is Azure Functions?

Azure Functions is an event-driven, serverless compute platform that empowers developers to streamline application development. It abstracts hardware intricacies, allowing focus on core business logic. Developers can simplify complex orchestration and locally develop and deploy scalable solutions in the cloud. It seamlessly connects functions to Azure services through triggers and bindings.

How Does Azure Functions Work?

Azure Functions operate on the principles of serverless computing, offering a straightforward and efficient way to execute event-triggered code without the need to manage the underlying infrastructure. Key aspects of how it works include:

  1. Triggers: Azure Functions respond to various event triggers, such as HTTP requests, database updates, message queue messages, and timers. Each function is associated with a specific trigger.
  2. Automatic Scaling: When a trigger is activated, it dynamically allocates the required compute resources for code execution, ensuring efficient scaling with varying workloads. Resources are automatically provisioned and de-provisioned, and you are billed only for the actual compute time used.
  3. Execution Environment: Developers write code in their preferred programming languages, such as C#, JavaScript, Python, or Java, within a “Function App,” providing a runtime environment for the functions.
  4. Stateless: Azure Functions are designed to be stateless, meaning they don’t maintain a persistent state between invocations. Any necessary state information should be managed externally, typically in a storage service or a database.
  5. Integration: Azure Functions seamlessly integrate with various Azure services and external systems, facilitating the creation of powerful, event-driven applications. They can connect to databases, trigger actions based on external events, store results in data stores or send notifications.

Who Uses Azure Functions?

Azure Functions is a versatile serverless computing service offered by Microsoft Azure, and it finds applications across a wide range of users and industries. Below is a table summarizing who uses them, including a short introduction to each user group.

User GroupUse CaseBenefits
DevelopersCloud-Native Applications: Developers use Azure Functions to build and deploy cloud-native applications.– Focus on writing code, not infrastructure management. 

– Automatically scale with increased workloads.

 Event-Driven Microservices: It facilitate the creation of microservices that respond to events.– Supports multiple event triggers (HTTP, database, queues, timers). 

– Seamless integration with Azure services.

 Real-Time Analytics: For real-time data processing and analytics, Azure Functions efficiently handles the workload.– Cost-effective serverless approach. 

– Easy integration with external systems and databases.

DevOps TeamsAutomation and Orchestration: DevOps teams use them to automate and orchestrate various tasks.– Task automation, such as continuous integration and deployment (CI/CD). 

– Scheduling, monitoring, and alerting capabilities.

 Infrastructure Monitoring: Azure Functions can monitor infrastructure health and trigger alerts in case of issues.– Proactive issue resolution. 

– Improved system reliability and stability.

Enterprises & StartupsScalable Solutions: They are a foundation for scalable solutions, handling varying workloads.– Cost-effective and scalable architecture. 

– Simplified resource management, reducing operational overhead.

 Data Processing: Enterprises use Azure Functions to process large volumes of data efficiently and cost-effectively.– Streamline data processing pipelines. 

– Support for data transformations, ETL, and real-time data processing.

 IoT and Edge Computing: Azure Functions are applied in IoT and edge computing scenarios, offering real-time data analysis.– Easy-to-implement event-driven IoT solutions. 

– Integration with Azure IoT services and edge devices.

Developing Code

Azure Functions provides a flexible and efficient platform for developing event-driven code, making it a valuable resource for various applications. This section will examine a mini project demonstrating how to develop code. 

Mini Project: Analyzing Your Spotify Playlist with Azure Functions

Step 1: Setting Up the Environment

Before you begin, ensure you have the following in place:

Azure Account: You’ll need an active Azure account. You can create a free account at Azure Portal if you don’t have one.

Azure Functions Tools: Ensure you have the Azure Functions Core Tools installed on your development machine. You can install them using npm:

npm install -g azure-functions-core-tools@3 --unsafe-perm true

Step 2: Creating the Function

Open your command line and navigate to the directory where you want to create your Azure Function project.

  • Run the following command to create a new function project:
func init SpotifyPlaylistAnalyzer
  • Navigate to the project folder:
cd SpotifyPlaylistAnalyzer
  • Create a new function with an HTTP trigger:
func new

Step 3: Setting Up Spotify API Integration

Create a local.settings.json file in your project directory with the following structure using the Spotify API credentials you obtained earlier:

{ 
  "IsEncrypted": false,
 
  "Values": {
 
    "AzureWebJobsStorage": "UseDevelopmentStorage=true",
 
    "FUNCTIONS_WORKER_RUNTIME": "node",
 
    "SpotifyClientId": "<Your Spotify Client ID>",
 
    "SpotifyClientSecret": "<Your Spotify Client Secret>"
 
  }
 
}

Modify the function.json file in your Azure Function project to specify an HTTP trigger.

Step 4: Writing the Function Code

Replace the code in index.js with your custom logic to connect to the Spotify API, retrieve data from your playlist, and perform your analysis.

You can use the node-fetch library to request HTTP to the Spotify API and process the data.

Step 5: Deploying the Function

Run the following command to deploy your function to Azure:

func azure functionapp publish <FunctionAppName>

Replace <FunctionAppName> with the name of the Function App you created in Azure.

Step 6: Testing the Function

After deployment, you can test your function by requesting an HTTP to the provided endpoint URL.

Step 7: Scheduling the Function

You can configure a timer-based trigger for your function to run at regular intervals, ensuring your playlist analysis remains up-to-date.

This mini project showcases how Azure Functions can be used to automate data processing tasks from external sources. It’s a practical example of developing code for these functions, demonstrating their value in real-world scenarios.

Reference: Using the Spotify API for data-driven analysis of my playlists

Azure Functions Pricing and Requirements

Azure Functions offer flexible pricing models tailored to the consumption and premium plans, ensuring cost-effectiveness for different use cases.

Consumption Plan

These functions in the consumption plan are billed based on per-second resource consumption and the number of executions. The pricing details include:

  • Free Grant: A monthly free grant of 1 million requests and 400,000 GB-s of resource consumption per month per subscription in the pay-as-you-go pricing.
  • Execution Time: Priced at $0.000016/GB-s.
  • Total Executions: Billed at $0.20 per million executions after the free grant.
  • Resource Consumption: Azure Functions in the consumption plan bill based on observed resource consumption, measuring in gigabyte seconds (GB-s). It calculates memory size and execution time. Memory is rounded up to the nearest 128 MB, capped at 1,536 MB, while execution time is rounded up to the nearest 1 ms. The initial 400,000 GB-s are part of the free grant.

Premium Plan

The Premium plan offers enhanced performance and VNET access. It is billed based on the number of core seconds and memory allocated across instances. There is no execution charge with the Premium plan, but you must allocate at least one instance at all times per plan.

Strengths and Limitations

StrengthsLimitations
Scalability: Automatically scales to handle increased workloads.Learning Curve: Some expertise is required to configure and optimize functions effectively.
Cost-Efficiency: Pay only for resources used during execution.Execution Time: Limited execution time for each function (up to 5 minutes in the Consumption plan).
Integration: Seamlessly integrates with Azure services and external systems.State Management: Lacks built-in state management; requires external storage for persisting data.
Event-Driven: Supports multiple event triggers, making it versatile for various scenarios.Limited Resource Scaling: Depending on the hosting plan, there are scaling and resource limitations.
DevOps-Friendly: Ideal for automating tasks and CI/CD processes.Integration Costs: Usage of other Azure services may incur additional charges.
Microservices: Enables the creation of event-driven microservices.Learning Curve: Some level of expertise is required to configure and optimize functions effectively.

Conclusion

Microsoft Azure Functions is one of the finest innovations in cloud computing. It simplifies event-triggered code execution and abstracts infrastructure management challenges by operating on the tenets of serverless computing. Developers can create incredibly effective, scalable, and responsive applications with its automatic scaling, stateless design, and simple connectivity with various Azure services. Azure Functions reflects a significant breakthrough in cloud-based solution development with its effective resource allocation and dynamic provisioning.

Analytics Vidhya offers Free Access to Fundamentals of Microsoft Azure Course. Access Now!

Frequently Asked Questions

Q1. Why are Azure Functions important?

Ans. Azure Functions are crucial for serverless computing, enabling automatic scaling, cost-efficiency, and easy deployment of microservices.

Q2. What are the functions of Azure?

Ans. They serve various roles in cloud computing, from event-driven automation to building microservices, data processing, and more.

Q3. Is Azure function a Microservice?

Ans. They can be used to implement microservices, but they are not limited to just microservices. They support various functions in the serverless paradigm.

Q4. What are the different function types in Azure?

Ans. Azure offers three primary function types: HTTP-triggered, timer-triggered, and event-triggered functions, making it versatile for a range of use cases.

Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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