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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
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Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
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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

Top 4 Cloud Computing Models You Should Know (2024)

Prashant
Prashant Last Updated : 02 Feb, 2024
5 min read

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

Introduction

Whether you’re a company owner interested in migrating your on-premise infrastructure to the cloud or a student interested in learning about cloud computing, the first step is understanding cloud computing models. Three models will be encountered: IaaS, PaaS, and SaaS. These models have a wide range of unique features, and you may readily access these cloud services over the Internet

Cloud Computing Models

Table of contents

What is Cloud computing?

Cloud computing is a term that refers to a system that utilizes the Internet to store and manage data on remote servers. The cloud-based data may subsequently be accessible over the Internet. It is a software solution that enables remote access to shared storage, servers, and applications through the Internet. The technology allows users to work remotely. It is one of the fastest-growing markets globally, expected to expand from $67 billion in 2015 to $162 billion in 2020, a 19 percent compound annual growth rate.

Cloud Computing Models

Cloud Computing Models

Top 4 Cloud Computing Models:

IaaS (Infrastructure as a Service)

Cloud computing paradigm IaaS supplies you with networking hardware through the Internet, and Virtualization makes these resources available to you. An IaaS platform allows you to utilize virtual machines (VMs) to install an operating system (OS) or software (software) and operate databases. Using this VM, you may run a virtual data center.

Cloud Computing Models, 1 laas

It is the third-party cloud vendor that provides the necessary computing resources and the necessary storage infrastructure. You only need to pay for their services to use them. IaaS enables customers to do system maintenance, disaster recovery planning, and backups. IaaS facilitates the automation and Virtualization of administrative duties within businesses, freeing up time and resources for other activities.

AWS (Amazon Web Services), DigitalOcean, and Microsoft Azure are all examples of IaaS

Advantages of using IaaS

  • It operates on a pay-as-you-go basis, allowing you to pay only for the services you utilize. You can always purchase additional resources later if necessary.
  • Because all of your data is stored on the cloud, there is no single point of failure. If a data centre or a server fails, the infrastructure functions normally.
  • The scalability of the infrastructure is determined by the storage and processing requirements.
  • Multiple users or staff can collaborate remotely on a project using IaaS. This minimizes workload and accelerates the execution of projects.
  • Reduces the cost of hardware acquisition and maintenance for the firm.

PaaS (Platform as a Service)

PaaS is the cloud computing stack’s intermediate layer. As the name implies, it provides a platform for developing and deploying cloud applications. It is mainly favoured by developers since PaaS companies host all necessary hardware and software for application development on their infrastructure. As a result, developers are relieved of installing software and worry about hardware.

2 Paas, Cloud Computing Models

This enables application developers to concentrate on their program and away from technical details. This results in more efficient software development and deployment. Additionally, PaaS allows you to design, test, execute, and update cloud-based applications. Additionally, you receive pre-built tools and APIs that enable you to modify your apps.

Google App Engine, Heroku, and Apache Stratos are all examples of PaaS.

Advantages of using PaaS

  • It is economical since you pay for the services you select.
  • The built-in data protection and cybersecurity capabilities lessen an organization’s reliance on security personnel.
  • PaaS enables the development of cross-platform applications, increasing your consumer base and aiding in business growth.
  • Dynamic scaling enables businesses that use PaaS to scale up or down in response to changing business requirements.
  • Because the vendor manages, both the hardware and software, the requirement for additional people or outsourcing duties is considerably minimized.

SaaS (Software as a Service)

SaaS is the foundation of the cloud stack, offering cloud-based applications on a monthly or annual subscription basis. The cloud vendor gives you Internet access to the program following the purchase. This is a browser-based software solution, and no additional software is required. You can use the service through user interfaces without incurring significant upfront costs

SaaS products such as DropBox and Salesforce are popular. Those looking for a complete cloud software solution without spending a lot of money can consider these possibilities. SaaS is an option for small and medium-sized organizations that are just getting started and lack the resources to invest in IT infrastructure. You won’t have any problems getting help from the SaaS providers because they give excellent customer service.

Cloud Computing Models

SaaS providers host all software infrastructure, middleware, and application data in their data centres. The vendor protects the security of your app data through the use of a service agreement. The nice aspect of SaaS is that it does not require you to be a technology expert in using it.

Advantages of using SaaS

  • You may access the SaaS software from a laptop, desktop, tablet, or smartphone using a web browser such as Google Chrome.
  • It is simple to use, adaptable, and scalable. The pay-as-you-go concept enables cost savings.
  • Users receive new versions and updates as soon as they become available. The expense of upgrading is negligible

Cloud Computing Deployment Models

There are three deployment models for cloud computing:

Public Cloud (Cloud computing in the public domain)

These cloud services, owned by a third-party provider, are available to the public for free. Due to the nature of open-ended access, security is compromised. It may be hazardous to use this cloud service because it is accessible to anybody, making it subject to hackers.

Private Cloud (Cloud computing services for individuals)

A particular company uses cloud computing resources. On-site or vendor-hosted data centers can be used for this service. The architecture is preserved over a private network and is only accessible to the organization.

Hybrid Cloud

A hybrid cloud is a cloud computing environment that combines private and public clouds, giving an organization increased flexibility and deployment options.

Conclusion

Cloud computing models have become an essential aspect of any company’s operations in today’s world. Companies may select the service that best suits their needs based on their size, technological requirements, and financial constraints. For many businesses across the globe, cloud computing has saved money and improved efficiency.

Read more articles about cloud computing models on our blog.

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

Prashant
Prashant

Hello, my name is Prashant, and I'm currently pursuing my Bachelor of Technology (B.Tech) degree. I'm in my 3rd year of study, specializing in machine learning, and attending VIT University.

In addition to my academic pursuits, I enjoy traveling, blogging, and sports. I'm also a member of the sports club. I'm constantly looking for opportunities to learn and grow both inside and outside the classroom, and I'm excited about the possibilities that my B.Tech degree can offer me in terms of future career prospects.

Thank you for taking the time to get to know me, and I look forward to engaging with you further!

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