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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

Compute Services Available on Microsoft Azure

anushkakhatri
anushkakhatri Last Updated : 04 Sep, 2022
5 min read

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

Introduction

Applications in Azure run on compute services, which determine how they are performed and allow cloud-based applications to be run on-demand. Resources are available on request within a few minutes or seconds, and you only pay for what you use. We will discuss Azure compute services and their applications in this article. Additionally, we will broadly discuss how it is classified into different services and how each is applied.

As part of Microsoft Azure services, the following services are available:

  • Azure Virtual Machine
  • Azure Virtual Machine Scale Sets
  • Azure App Services
  • Azure Container Instances
  • Azure Kubernetes Services
  • Azure Functions
  • Azure Virtual Desktops

To the needs of each client, each service will provide various options.

Microsoft Azure
Source: https://vegibit.com/azure-cloud-services/

Microsoft Azure Virtual Machines

Azure Virtual Machines
Source: https://www.educba.com/azure-virtual-machines/

Virtual machines on Azure are virtualized versions of physical computers that contain a virtual processor, memory, storage, and networking interface. Virtual machines (VMs) provide a framework as a service or IaaS. Azure offers VM images pre-installed with the operating system and other software tools. By doing this, you will not have to install anything yourself. These machines are flexible and can be used in a variety of ways. It only requires selecting the operating system, configuring the resources, and establishing a web server.

Microsoft Azure Virtual Machine Scale Sets

Using Virtual Machines Scale Sets, you can manage similar virtual machines, configure, and update them simultaneously to provide highly available applications. Large-scale services that need a large computation, big data, containerized workloads, and big compute can be built with the capabilities of virtual machine scale sets since they can automatically increase or decrease the number of instances based on the demand. Manual or automated processes can be used to add or remove virtual machines. 

Azure App Services

App Services
Source: https://weblogs.asp.net/scottgu/announcing-the-new-azure-app-service

Azure App Service is an example of a platform as a service (PaaS). Therefore, only organizations or you are responsible for managing your business application. Azure handles the rest. There is no need to worry about managing the networks and their underlying infrastructure. Install operating system updates, critical patches, runtime,s,  and middleware components. Azure handles all of these. This gives you even more time to focus on your business. The Azure App Service supports a wide variety of programming languages and frameworks such as .NET, .NET Core, Java, Ruby, Node.js, and PHP. App Service can scale resources up or down as your application’s demand changes. This can be done manually or automatically based on metrics such as the CPU’s utilization.

Azure Container Instances

The Microsoft Azure Container Instances service enables us to run these containers directly on Azure without the need for virtual machines. Using Azure Container Instances, you don’t need to worry about provisioning infrastructure or to use higher-level services. Using ACI, you can manage a group of this container on a host machine. For more progressive tasks like coordinated upgrades, service discovery, and automated scaling, it supports complete container orchestrators like Kubernetes.

Key features of Azure Container Instance are:

  • Container support for Linux and Windows
  • A new container can be conveniently launched using the Azure portal or command line interface (CLI); underlying compute resources are automatically configured and scaled.
  • Support for Docker images and public container registries, including Docker Hub and Azure Container Registry
  • A fully qualified domain name and IP address can be used to access content on the Internet.
  • A container instance CPU cores and memory can be stated explicitly.

Microsoft Azure Kubernetes Services

Kubernetes Services

Source: https://www.whizlabs.com/blog/azure-kubernetes-service/

Microsoft Azure Kubernetes Service is a managed container orchestration service based on the open-source Kubernetes system. Any organization can deploy, scale, and manage Docker container and container-based applications using AKS.

The AKS platform became available in June 2018 and is mainly used by software developers and IT operations personnel. The AKS platform also depletes the complexity and overhead associated with deployment and management. AKS provides administrators and developers flexibility, automation, and reduced management overhead.

In addition to automatically configuring all Kubernetes nodes and controlling and managing worker nodes during deployment, AKS also handle many other tasks, such as the integration of Azure Active Directory, connecting monitoring services, and configuration of advanced networking features such as HTTP application routing. Azure Monitor allows users to monitor clusters directly or view all clusters simultaneously.

Microsoft Azure Functions

microsoft azure

Source: https://kasamshaikh.com/learn-to-build-event-driven-net-apps-using-azure-functions/

Azure functions are serverless concepts of cloud-native design that allow code to be deployed and compiled without any server infrastructure, web servers, or configuration. Several languages can be used to write Azure functions, including C#, Java, JavaScript, TypeScript, and Python. Functions in Azure are scalable. Whenever the number of requests increases, more resources are assigned to the service, and when the number of requests decreases, all the extra resources, and applications drop off automatically.

Advantages of using azure functions:

  • Functions in Azure are insubstantial and serverless.
  • It is easier to write and deploy Azure functions.
  • Due to the lack of a large application, start-up time, initialization, and other events before the execution, Azure functions are fast to perform. An event trigger causes the execution of Azure functions.
  • Functions in Azure are compute-on-demand and scalable.
  • Whenever the service increases requests, it automatically allocates more resources and drops all the extra resources and application instances.

Microsoft Azure Virtual Desktops

Azure Virtual Desktop is an instanced virtual machine that hosts desktop and app virtualization services in the cloud. Using Azure Virtual Desktop, users can access virtual desktops and remote apps from any device. The platform can combine Microsoft 365 and Azure to provide multi-session Windows 10 experiences, including scalability and IT cost reduction, depending on its configuration.

The modern workforce has become remote and mobile, whether by necessity or because it is a productivity strategy. The workforce is surprisingly dependent on cloud platforms and virtual machines to access the systems and applications it needs to do its jobs anywhere, anytime, and on any device. An Azure Virtual Desktop provides access to a virtual Windows computer that can run typical apps a typical IT-approved Windows computer should run for the organization. Azure’s cloud infrastructure allows businesses to deploy multi-session Windows 10 deployments optimized for multi-user virtual environments. As far as the workers are concerned, Azure Virtual Desktop is similar to a PC on their desk. By entrusting Microsoft and Azure to handle those specifics in the cloud, enterprises can save time and money on purchasing, setting up, deploying, and securing hardware.

Conclusion

In this article, we discussed Azure Compute in conjunction with the various services Azure provided; a few are Azure App Service, Azure Virtual Machines, Azure Container Instances, and Azure Functions. We learned about these different topics and explored Virtual Machine Scale Sets and the decision-making regarding the need to use Azure Virtual Machines. We discussed the various features of Azure Functions like the event-driven scale, micro-billing, and abstraction of servers. We learned about the Azure Logic Apps and got in touch overall with Azure Compute.

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

azure batchblogathonMicrosoft azure
anushkakhatri
anushkakhatri

Passionate about solving business problems with data-driven solutions. Skilled in the field of Data Science and Big Data Engineering.
I have worked on various projects, including developing predictive models, analyzing complex data sets, and designing and implementing data architectures and pipelines.
I enjoy exploring new data science and data engineering techniques and keeping up with the latest industry trends.

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