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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

Understanding the Basics of Cloud Computing

Amrutha
Amrutha Last Updated : 01 Mar, 2022
5 min read

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

Table of contents

  1. Introduction
  2. What is Cloud Computing?
  3. Service Models
  4. Deployment Models
  5. On-Premise vs Cloud Computing
  6. Conclusion

Introduction

In this article, we will learn about the basics of cloud computing. We will see what are different service models, what are different deployment models and finally we will understand the difference between on-premise architecture and cloud computing.

Cloud Computing refers to the delivery of computing services including storage, servers, databases, networking, software over the internet. To offer faster innovation flexible resources. Here you have to pay only for those cloud services which you use. So that you can help with your operator cost.

Nowadays many leading companies like Amazon are investing billion dollars in cloud services.

What is Cloud Computing?

First, we will understand the situation that existed before cloud computing came into existence.

In order to host a website, people used to face these problems.

1. Buying a stack of servers which are very costly.

2. With more number of servers usually there is high traffic.

3. Troubleshooting problems.

4. Monitoring and Maintaining servers is also very difficult.

In those days the data that is being generated is not an issue to store. But nowadays data is very huge. Everything is online these days. To play music, to watch movies, Business matters, to shop online, e-books, Applications for everything data is being generated and to store this is a huge task.

All these problems which I have mentioned so far are taken care of by the cloud.

Now let’s understand the cloud in detail.

Think of the cloud as a huge space that is available online. Cloud is like a collection of data centers where u host your websites and store your files. To understand better, there were a group of organizations that went and bought these servers, these compute services, storage places, and all. And all these have their own network. What you have to do is just go and rent these services. That too the amount of it you want and to what extent you need it. You have to rent and use what you want. So you ended up paying for what you used.

Cloud Computing is storing data on remote servers, processing data from servers, and Accessing data via the internet. You can actually access it from anywhere in the world.

What is Cloud Computing?
Source: Author

Service Models in Cloud Computing

Basically, there were different types of people like there were some people who just uses the cloud to use only one particular resource and there were some who will create their own applications, create infrastructure, and all those things. So Cloud service providers came up with some models which can satisfy different people’s needs.

Let us try to understand these models.

There were three types of service models.

Source:https://edge.siriuscom.com/wp-content/uploads/2022/01/3-Popular-Cloud-Service-Models.png

 Let us start with SaaS.

SaaS(Software as a Service)

What happens here is you just use the software that is already being created and maintained by someone else. To understand it, think of Gmail where you can actually send and receive emails. But you neither created nor being maintained. Google will take care of everything. Similarly here also you just consume the service.

Example: salesforce.com provides CRM which is Customer Relation Manager on cloud infrastructure to its client and charges for it but the software is owned by salesforce company only.

PaaS(Platform as a Service)

Here cloud provider gives the ability to deploy customer-created apps using programming languages, tools, etc that are provided by the Cloud Provider. To give an example, we have a Google App engine where you can create your own applications. Here you are using the Platform. Similarly, PaaS also provides a platform for creating your own applications.

IaaS(Infrastructure as a Service)

  IaaS provides the whole infrastructure to you so that you can create your own application. The whole underlying infrastructure is provided to you so that you can choose whatever Operating system you want to use, technologies you want to use, and application you want to build.

IaaS(Infrastructure as a Service)
Source: Author

With this, I think you can understand better. Here you can see in SaaS only Data is being managed by you. Everything else like applications, runtime, middleware, operating system, virtualization, servers, storage, and networking are being managed by the vendor. Coming to Paas, Data and applications are managed by you and everything else is managed by the vendor. Now finally for IaaS, Data, applications, runtime, middleware, operating system are managed by you and basic things like virtualization, servers, storage, and networking are managed by the vendor.

Deployment Models in Cloud Computing

Cloud deployment models are divided based on the security control, who has access to data, and also based on whether the resources are shared or not. It represents the particular category of the cloud environment.

Basically, there are three main types of deployment models.

1. Public Cloud

2. private Cloud

3. Hybrid Cloud

Let us understand one by one.

Public Cloud: As the name suggests it is available to the general public over the internet. Here provider makes all resources and makes them available publicly. It is very easy and inexpensive because all the hardware, application, and bandwidth costs are covered by the provider. Resources are also not being wasted because you will pay for what you use.

Private Cloud: Here actually you can create your own applications and you are protected by a firewall so that security issues are being minimized.

For example, HP data centers, Ubuntu, Elastra-private cloud, Microsoft.

Hybrid Cloud: Hybrid cloud is the combination of both private cloud and public cloud. You can build your own application and access it. And whenever you feel like there is traffic you can move it to the public cloud and use it.

The top 5 Hybrid Cloud Providers are Amazon, Microsoft, Google, Cisco, Netapp.

On-Premise vs Cloud Computing

To understand it first, we will see what is on-premise architecture.

On-Premise Approach

This is like the traditional approach. You will write your own piece of code and you will own your own server. As a company, employees are maintaining them and making sure the software is deployed properly. Remaining everything like monitoring these applications, having servers, maintaining them all those you should take care of. There is a high up-front cost and maintenance cost. Here owner manages security.  When it comes to on-premise, you have more control.

Cloud Computing Approach

It is a huge space available online with a stack of servers that are orchestrated to provide you with various services like databases, storage, computation, security, and many more. There is a cheaper up-front cost. When it comes to security, it is vendor dependence. Owners are expected to give up control. Here you have less control over it. You have to be a little bit dependent on your vendor.

Conclusion

In this article, we have learned the basics of Cloud Computing like what exactly cloud computing means, how this actually helps us, different service models, different Deployment models, and comparing On-Premise and Cloud computing.

Hope you guys found it useful.

Connect with me on LinkedIn: https://www.linkedin.com/in/amrutha-k-6335231a6vl/

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Amrutha
Amrutha

This is Amrutha, I am pursuing B.Tech in the Computer science Department. I am interested in developing ML Models with python and Data Analysis. And also I have an interest in Web Development. I hope my articles in Analytics Vidhya help you to learn better. Thank you!!

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