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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

Types of Cloud Computing Platforms

Aashish
Aashish Last Updated : 28 Jun, 2023
9 min read

Introduction

Cloud computing has emerged as a transformative force, revolutionizing how organizations access and utilize computing resources. As businesses increasingly rely on the cloud for their IT infrastructure, it becomes essential to understand the different types of cloud computing platforms available. Each platform offers distinct advantages and features, catering to specific needs and objectives. This article delves into the diverse landscape of cloud computing platforms, exploring the nuances of public, private, and hybrid clouds. By understanding these platforms comprehensively, businesses can make informed decisions when selecting the right cloud environment to meet their unique requirements.

Learning Objectives

  1. Understanding the concept of cloud computing & its elements
  2. Types of cloud computing platforms
  3. Comparing and contrasting the advantages & disadvantages of cloud computing

This article is a part of the Data Science Blogathon.

Table of contents

What is Cloud Computing?

By harnessing the potential of remote servers spread across the internet, cloud computing liberates us from physical infrastructure constraints. Rather than depending on localized servers, cloud technology empowers users to store data and access web-based applications from any location with an internet connection. This newfound flexibility and accessibility revolutionize how we manage information and utilize software, enabling seamless connectivity and productivity across diverse environments.

NASA released image
Source: Unsplash

Benefits of Cloud Computing

The main benefits of cloud computing include the following:

  • Cost savings – Cloud computing eliminates the need to purchase and maintain physical servers, which can be expensive. Additionally, cloud services are often billed on a pay-as-you-go basis, so you only pay for what you use.
  • Scalability – Cloud computing allows businesses to scale up or down depending on their needs. This can be done quickly and with minimal disruption.
  • Flexibility – Cloud computing gives users access to their data and applications using a device with an internet connection.

Cloud Computing Platforms
Source: Unsplash

Several different cloud computing platforms are available, each with advantages and disadvantages. Here’s an overview of the most common types of cloud computing platforms:

Platform-as-a-Service (PaaS)

Platform-as-a-Service (PaaS) emerges as a dynamic cloud computing solution, providing users with a comprehensive suite of hardware, software, and resources to seamlessly develop, deploy, and manage applications without additional hardware or software investments. PaaS proves invaluable for developers and individuals tasked with creating custom applications or seamlessly integrating existing ones into the cloud environment. With PaaS, innovation knows no bounds as users unlock the power to shape their digital landscape without the complexities of infrastructure provisioning.

Use Cases for PaaS

  1. Web Application Hosting: PaaS can host applications requiring frequent updates without managing the underlying infrastructure. This makes it easier to deploy and scale applications.
  2. Mobile App Development: PaaS can be used to develop and deploy mobile applications more quickly, as it provides access to ready-made components and services.
  3. Big Data Analytics: PaaS can process and analyze large amounts of data quickly and cost-effectively, as it provides access to powerful computing resources.
  4. IoT Solutions: PaaS can be used to develop and manage connected devices and applications, as it provides access to scalability and secure communication infrastructure.
  5. DevOps Automation: PaaS can be used to automate development and operations processes, such as deployment, testing, and monitoring, which helps to ensure faster and more reliable software releases.

Examples of PaaS

  • Windows Azure: Windows Azure is Microsoft’s cloud computing platform, which provides an operating system, a set of services, and tools for developers to create, deploy and manage applications in the cloud. It supports various programming languages and frameworks, such as .NET, Java, PHP, and Node.js, and provides data storage, networking, computing, and analytics services.
  • Heroku: Heroku is a cloud-based Platform as a Service (PaaS) that allows developers to construct, operate, and expand programs on the cloud. It is powered by a managed container system that makes it easier to distribute, scale, and manage applications. It is compatible with many programming languages and frameworks, such as Ruby, Java, Node.js, and Python, and features a wide range of services and added features.

Infrastructure-as-a-Service (IaaS)

Infrastructure-as-a-Service (IaaS) is a cloud computing solution that furnishes users with virtualized computing components such as servers, storage, networks, and operating systems. It is optimal for those seeking more control over their infrastructure while avoiding physical hardware costs.

Use Cases for IaaS

  1. Web Hosting: IaaS can host web-based applications and websites, providing users access to the underlying infrastructure and computing resources.
  2. Application Development and Testing: IaaS can be used to develop and test software applications, as it provides users with access to the underlying infrastructure and computing resources.
  3. Database Hosting: IaaS can host databases as it provides users access to the underlying infrastructure and computing resources.
  4. Disaster Recovery: IaaS can be used for disaster recovery, as it allows users to quickly provision additional resources from the cloud to restore their data and systems.
  5. Big Data Analytics: IaaS can store, process and analyze large amounts of data, providing users with access to the underlying infrastructure and computing resources.
  6. IoT Deployment: IaaS can deploy and manage large-scale Internet of Things (IoT) solutions, as it provides users with access to the underlying infrastructure and computing resources.

Service Examples of IaaS

  • DigitalOcean: DigitalOcean is a cloud infrastructure provider that offers virtual private servers (VPSs) known as “droplets”. Droplets are flexible, easy to configure, and cost-effective, making them ideal for hosting websites and applications. DigitalOcean provides additional tools and services such as a managed database service, Kubernetes clusters, and one-click applications.
  • Linode: A cloud infrastructure provider that offers virtual private servers (VPSs). Linode provides a wide range of VPS plans and custom solutions to fit the needs of any size organization. They also offer a suite of developer tools and services such as managed databases, object storage, and load balancers.
  • Rackspace: Rackspace is a cloud infrastructure provider that offers managed cloud services, such as virtual private servers (VPSs), dedicated servers, and cloud servers. They provide various services and solutions, including managed databases, application hosting, and cloud storage. Rackspace also offers managed security services and 24/7 customer support.

Software-as-a-Service (SaaS)

Software-as-a-Service (SaaS) revolutionizes the way software is accessed and utilized. As a cloud computing solution, it provides users seamless access to software applications via the internet. These web-based programs can be utilized from any device with an internet connection, eliminating the need for local installations. SaaS caters to individuals and organizations seeking efficient access to specific software programs, enabling enhanced collaboration, scalability, and flexibility without the burden of software management.

Use Cases for SaaS

  1. Email and Collaboration: Email and collaboration tools such as Google Apps and Office 365 are popular SaaS applications for communication and productivity.
  2. CRM: Customer relationship management (CRM) tools such as Salesforce and Zendesk provide businesses with a platform to manage customer data, automate sales and marketing operations, and track customer engagement.
  3. E-commerce: E-commerce platforms such as Shopify, BigCommerce, and Magento provide businesses with a complete solution to create and manage their online stores.
  4. Project Management: Project management and task management tools such as Asana, Trello, and Basecamp are popular SaaS applications used to manage projects, tasks, and timelines.
  5. Accounting: Accounting and bookkeeping tools such as QuickBooks Online and Xero provide businesses with an easy way to track financials and keep their books in order.
  6. Human Resources: Human resource management (HRM) tools such as BambooHR and Zenefits provide businesses with a platform to manage employee data and automate HR processes.

Service Examples of SaaS

  • Google Workspace (formerly GSuite): Google Workspace (formerly GSuite) is a suite of cloud-based productivity and collaboration tools that includes Gmail, Docs, Sheets, Slides, Calendar, Meet, and more. It provides businesses with an easy way to collaborate, share documents, and store data in the cloud.
  • Dropbox: Dropbox is a file storage and sharing service that enables users to store and access their data from any device with an internet connection. It also provides real-time collaboration tools to collaborate on projects in the cloud.
  • Salesforce: Salesforce is a customer relationship management (CRM) platform that provides businesses with the tools they need to manage customer relationships and data. It enables companies to store, track, and analyze customer data and automate and streamline sales processes.

Function-as-a-Service (FaaS)

Function-as a Service (FaaS) is a cloud computing solution that enables developers to upload code directly onto the cloud without the need to handle servers or virtual machines. FaaS is optimal for executing small code pieces rapidly and effectively without managing servers or virtual machines.

Use Cases for FaaS

  1. Image Processing and Analysis: FaaS can quickly and efficiently process images. Applications of this could include facial recognition, object recognition, or text recognition.
  2. Machine Learning: FaaS can quickly and efficiently build and deploy machine learning models in the cloud. These models can predict customer behavior, detect anomalies, or automate decision-making.
  3. IoT: FaaS can quickly and efficiently deploy and manage applications on IoT devices. This could include applications for monitoring and managing connected devices, analyzing data from sensors, or controlling devices remotely.
  4. Web Applications: FaaS can quickly and efficiently deploy and manage web applications in the cloud. This could include content management, e-commerce, or customer relationship management applications.
  5. Data Processing and Analytics: FaaS can be used to quickly and efficiently process and analyze large datasets in the cloud. This could include applications for data warehousing, predictive analytics, or text analytics.

Service Examples of FaaS

  • Amazon Web Services (AWS) Lambda: AWS Lambda is a serverless computing platform that allows developers to run code without having to manage or provision any servers. It is a service that allows users to upload their own code and then run it in the cloud without needing servers or maintenance. Lambda functions can be triggered by events, such as a file uploaded to a S3 bucket or a change in a DynamoDB table.
  • Azure Functions: Azure Functions is a serverless computing service from Microsoft that allows users to run code without having to manage or provision any servers. It is a service that allows users to upload their own code and then run it in the cloud without needing servers or maintenance. Azure Functions can be triggered by events, such as a file uploaded to a Blob container or a change in a Cosmos DB database.
  • Google Cloud Functions: Google Cloud Functions is a serverless computing service from Google that allows developers to run code without having to manage or provision any servers. It is a service that allows users to upload their own code and then run it in the cloud without needing servers or maintenance. Google Cloud Functions can be triggered by events, such as a file being modified, etc.

Cloud Computing Providers and Their Services

Several cloud computing providers offer different types of services for businesses of all sizes. Some of the most popular cloud providers include Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), IBM Cloud, Oracle Cloud Infrastructure (OCI), and Alibaba Cloud. Each provider offers different services, such as computing services, storage services, database services, analytics services, machine learning services, artificial intelligence services, security services, and more.

Conclusion

To sum up, various types of cloud computing platforms are accessible to businesses of all sizes, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Software as a Service (SaaS), and Function as a Service (FaaS). It is crucial to understand your needs before choosing a platform, as each type has its pros and cons. Furthermore, multiple cloud providers offer diverse services, making it vital to conduct thorough research and determine the best fit for your business requirements before selecting.

For readers who are just getting started with cloud computing, here are some tips for choosing the right platform:

  • Consider your Budget – When selecting a platform, consider how much you’re willing to spend on it. Some providers offer free trial periods so you can try their services before committing to a long-term plan.
  • Think About Scalability – Think about how quickly you need to scale up your services and whether or not the platform you’re considering supports this feature.
  • Understand your Needs – Make sure you understand what type of service you need before selecting a provider so you can make an informed decision.
  • Research Different Providers – Take the time to research different providers and compare their features before selecting one. This will help you make sure you’re getting the best deal possible.

Frequently Asked Questions

Q1. What are cloud computing platforms?

A. Cloud computing platforms are virtualized environments that provide on-demand access to computing resources such as virtual machines, storage, and networking. They allow users to deploy and manage applications and services without needing on-premises infrastructure.

Q2. What are the 4 platforms of cloud computing?

A. The four platforms of cloud computing are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), and Function as a Service (FaaS). Each platform offers different levels of abstraction and functionality for users.

Q3. What is the best cloud computing platform?

A. The best cloud computing platform depends on specific requirements and use cases. Popular platforms include Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). The choice should consider scalability, performance, cost, supported services, and integration options.

Q4. What are the three types of cloud platform?

A. The three types of cloud platforms are public cloud, private cloud, and hybrid cloud. Public cloud platforms are operated by third-party providers and accessible over the internet. Private cloud platforms are dedicated to a single organization. Hybrid cloud platforms combine public and private clouds, offering flexibility and scalability while controlling sensitive data.

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Aashish
Aashish

Product Manager with 10+ years of experience in driving product innovation, managing product development and leading agile product teams. Experienced in developing product strategies and executing on tactical plans to ensure successful product launches and profitable product roadmaps. Skilled in developing product roadmaps, managing product portfolios, and creating product requirements.

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