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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

Different Types of Cloud-based Services

anushkakhatri
anushkakhatri Last Updated : 29 Jul, 2022
4 min read

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

Introduction

Cloud computing is one of the fastest-growing IT technologies today. By 2025, 83% of enterprise workloads will be in the cloud. According to the IDC report, the world will spend $160 billion on cloud services and infrastructure in 2018. The cloud encompasses a variety of online services. It is important for businesses considering cloud services to understand the differences between IaaS, PaaS, and SaaS. You should choose the right model depending on your business requirements and the number of tasks you want to delegate or handle yourself. Selecting the ideal cloud service is undoubtedly difficult, and you must weigh several factors before deciding. Here, we have covered all of the benefits and drawbacks of SaaS, PaaS, and IaaS to help you decide which model is best for your company.
cloud-based service
https://litslink.com/blog/iaas-paas-saas

IaaS

Essentially, this is a virtual version of a physical data center. Cloud infrastructure providers use virtualization technology to deliver scalable computing resources to their clients, including servers, networks, and storage. Clients benefit from this since they cannot purchase personal hardware and manage its components. Instead of deploying their applications and platforms in physical data centers, they can use the provider’s virtual machines. The IaaS provider manages the entire infrastructure, but users fully control it. Apps and operating systems are installed and maintained by users, as well as security, runtime, middleware, and data.

Advantages of IaaS:

1. Model with the greatest flexibility and dynamic potential.
2. Due to pay-as-you-go pricing, it is cost-effective.
3. Automated hardware deployment makes it easy to use.
4. Employees have more time for other tasks because management tasks are virtualized.

Disadvantages of IaaS:

1. Multitenant architecture poses security risks.
2. Customers cannot access their data for some time due to vendor outages.
3. Learning how to manage new infrastructure requires team training.

PaaS

PaaS, or Platform as a Service, is an internet-based framework for creating software. This platform contains software components and tools developers can use to create, customize, test, and launch applications. Vendors of PaaS manage servers, operating system updates, security patches, and backups. App developers and data managers do not have to worry about maintaining infrastructure, middleware, and operating systems. IaaS and PaaS differ primarily in how much control users are given.

Advantages of PaaS:

1. Since PaaS-built software is cloud-based, it is highly scalable, available, and multi-tenant.
2. Streamlining and speeding up the development process.
3. The cost of creating, testing, and launching apps is reduced.
4. Policy automation.
5. A reduction in the amount of coding required.

Disadvantages of PaaS:

1. Issues related to data security.
2. It is not possible to cloud-enable every element of existing infrastructure.
3. Speed, reliability, and support depending on the vendor.

SaaS

The vendor provides cloud-based software with this offering. SaaS applications need not be downloaded and installed on local devices, but plugins may be necessary occasionally. SaaS software can be accessed via the web via APIs and is located on a distant cloud network. Customers can work together on projects, store data, and analyze it with these apps.
The most popular type of cloud computing is SaaS. Everything, from hardware stability to app functionality, is managed by the SaaS provider. In this arrangement, clients are exclusively responsible for using programs to carry out their jobs. In this instance, the provider controls the client’s software experience.

Advantages of SaaS:

1. There are no hardware costs, and initial setup costs are not incurred.
2. Upgrades are performed automatically.
3. Adaptability to multiple devices.
4. Easily accessible from anywhere, A pay-per-use model.
5. Adaptability, Customization is easy.

Disadvantages of SaaS:

1. A lack of control.
2. Solutions are limited.
3. The need for connectivity cannot be overstated.

Difference between IaaS, PaaS, and SaaS

cloud-based services

https://www.quora.com/What-is-the-difference-between-IaaS-SaaS-and-Paas

IaaS PaaS SaaS
1.    IaaS stands for Infrastructure as a Service
 PaaS stands for Platform as a service.
 SaaS stands for software as a service.
2.    As part of IaaS, the cloud provider manages the
infrastructure.
 As a part of PaaS, the cloud provider manages
both infrastructure and platform. 
 As part of SaaS, the cloud provider manages all
infrastructure, platforms, and software.
3.    In IaaS, you need to manage the platform and software. In PaaS, you need to manage the software.  As a SaaS user, you do not need to manage
anything.
4.    Infrastructure as a Service(IaaS) can be used for data recovery, storage, software testing, and development. Platform as a service can be used for framework
development and management, as well as business intelligence.
  Software as a service (SaaS) enables users to use applications for personal or business purposes.
5.    IaaS platforms are highly scalable.
 PaaS platforms allow customers to scale computing resources according to their business size. The SaaS model is scalable with different pricing tiers for every business level.
6.    Providers fully control infrastructure. Due to the limited capabilities of PaaS platforms, users may not be able to customize cloud operations.  An internet connection and a browser are all required for users to use SaaS apps.

Conclusion

After reading through all of the material above, you will understand the value of cloud services and why they are so well-liked by companies. The advantages of cloud solutions are enormous. Thus, it is unsurprising that this economic sector is experiencing such a sharp expansion. Choosing any model from the list above depends entirely on your business needs and the kind of task you want to carry out.

Key takeaways:

  • Several technical words are related to cloud computing, but IaaS, PaaS, and SaaS are the most crucial.
  •  They are regarded as the three key pillars that provide stability for cloud computing.
  • Your business goals will determine which of these three types you ultimately select.
  •  The three cloud models each have unique traits and capabilities.
  •  It is crucial to comprehend their main distinctions from one another.
  • Whether you want to use the cloud for storage, require a powerful platform to create custom apps, or want complete control over your infrastructure, you must pick the correct cloud provider.
  • Moving to cloud services is necessary to maintain your company’s organizational strength regardless of your option.
  • You can choose to use just one cloud computing model or all three models’ services.
  •  The size and complexity of your organization will determine this.

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

blogathonCloud ComputingIaaSPaaSsaas
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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