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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

AWS vs Azure: The Ultimate Cloud Face-Off

A
Analytics Vidhya Last Updated : 22 Feb, 2024
8 min read

Introduction

Amazon Web Services and Microsoft Azure are the two titans in cloud computing. The competition between these two industry leaders has sparked the “cloud war.” This article delves into the comprehensive comparison of AWS vs Azure, examining their features, advantages, disadvantages, job opportunities, and more.

Table of contents

What is AWS?

Amazon Web Services (AWS) is a feature-rich cloud computing platform offered by Amazon. It provides many on-demand services, including computing power, storage options, databases, machine learning, analytics, etc. These services enable businesses and people to create, distribute, and administer software applications and services without physical hardware, boosting the cloud environment’s flexibility, scalability, and affordability.

AWS

What is Azure?

Azure is Microsoft’s cloud computing platform, which offers various services such as processing power, storage, databases, networking, analytics, and more. It enables companies to build, implement, and manage cloud-based applications and services while providing scalability, flexibility, and integration with Microsoft’s software ecosystem.

microsoft azure

AWS vs. Azure: Overview

Let’s look at the basic difference between AWS and Azure:

AspectAWSAzure
CompanyAmazonMicrosoft
Launch Year20062010
Market ShareLeading market shareSecond-largest market share
ServicesBroad range of servicesWide variety of services
Compute ServicesEC2, LambdaVirtual Machines, Azure Functions
Storage ServicesS3, EBSBlob Storage, Azure Files
Database ServicesRDS, DynamoDBSQL Database, Cosmos DB
Networking ServicesVPC, Direct ConnectVirtual Network, ExpressRoute
AI & Machine LearningAWS AI/ML ServicesAzure AI/ML Services
Internet of Things (IoT)AWS IoT CoreAzure IoT Hub
Analytics & Big DataAmazon Redshift, EMRAzure Data Lake, HDInsight
PaaS ServicesAWS Elastic BeanstalkAzure App Service
Identity & Access ManagementAWS IAMAzure Active Directory
Pricing ModelPay-as-you-go, with free tierPay-as-you-go, with free tier
Hybrid Cloud SolutionsAWS Outposts, VPN, Direct ConnectAzure Stack, Hybrid Connections
Certifications & TrainingAWS Certified, AWS TrainingAzure Certified, Microsoft Learn
Customer BaseEnterprises, startups, developersEnterprises, government, developers
Geographic PresenceGlobal data centers in many regionsGlobal data centers in many regions

Azure vs. AWS: Computation Power

The computational power provided by cloud platforms is crucial in determining how firms can operate in the dynamic world of cloud computing. The leaders in this battle are Microsoft Azure and Amazon Web Services (AWS), which provide various computational resources to meet different processing requirements. 

This comparison examines the computing capacity offered by Azure and AWS, illuminating their virtual machine (VM) offers, scalability, and performance to aid organizations in making knowledgeable selections.

  • Virtual Machines (VMs): Both platforms provide VM instances that can be tailored to suit specific requirements, facilitating scalable and on-demand computing power.
  • Instance Types: Azure’s VMs come in various instance families like General Purpose, Compute Optimized, Memory Optimized, and more. Similarly, AWS offers instance types such as EC2’s M-series, C-series, R-series, and more, catering to different workload demands.
  • Scalability: Azure’s Virtual Machine Scale Sets and AWS’s Auto Scaling ensure applications can automatically adjust to varying workloads, optimizing resource allocation.
  • Performance: Azure’s Ultra Disk offers high throughput and low latency storage for VMs. AWS provides EC2 instances with varying CPU, memory, and storage performance levels.
  • GPU Instances: Both platforms offer GPU instances for accelerated computing tasks like machine learning, data analytics, and scientific simulations.

Azure vs. AWS: Cloud Storage

In the dynamic landscape of cloud computing, effective data storage solutions play a pivotal role in shaping an organization’s digital strategy. The rivalry between Microsoft Azure and Amazon Web Services (AWS) fuels a compelling comparison of cloud storage offerings. Both platforms offer an array of storage services tailored to diverse business needs. Exploring the nuances of Azure vs. AWS cloud storage unveils a spectrum of features, performance benchmarks, and cost structures. 

This comparison sheds light on key aspects, aiding businesses in making informed decisions that align with their storage requirements, scalability demands, and budget considerations.

  • Storage Services: Azure presents Blob Storage, Files, and Data Lake Storage, while AWS offers Amazon S3, EBS, and EFS, addressing various storage use cases.
  • Scalability: Both platforms provide scalable solutions, enabling organizations to adapt storage resources as workloads evolve.
  • Performance: Azure and AWS storage services emphasize consistent performance, catering to different data access patterns.
  • Integration: Azure’s alignment with Microsoft tools complements Microsoft-centric environments, while AWS’s vast service ecosystem caters to a broader array of applications.
  • Analytics: Azure Data Lake Storage and AWS S3 support data analytics, with variations in compatibility and integration.
  • Cost Optimization: Azure and AWS offer cost-effective models like tiered storage and storage classes, facilitating optimal expenditure management.
  • Decision Factors: Organizations must weigh integration preferences, performance, analytics support, and pricing structures when selecting between Azure and AWS cloud storage, ensuring seamless data management and growth.

Azure or AWS: Which is Better in Terms of Pricing?

Selecting the ideal cloud provider, Microsoft Azure or Amazon Web Services (AWS), involves a thorough evaluation of pricing structures. Azure offers pay-as-you-go, reserved, and spot instances, accommodating diverse budgets and workloads. AWS follows a similar model with on-demand, reserved, and spot instances, complemented by a vast range of services. 

The choice between Azure and AWS pricing hinges on a detailed analysis of usage patterns, service requirements, and cost optimization strategies. Balancing features and costs is crucial to ensure that the chosen platform aligns with business objectives while remaining financially viable.

AWS vs. Azure: Databases

Databases are the backbone of modern applications, storing and managing critical data. In the dynamic landscape of cloud computing, Amazon Web Services (AWS) and Microsoft Azure present a suite of database services catering to various application needs. 

This comparison explores the database offerings of both platforms, shedding light on their features, scalability, and suitability for different use cases.

AWS Databases

  • Amazon RDS: AWS offers Relational Database Service (RDS) for managed relational databases, supporting popular engines like MySQL, PostgreSQL, and SQL Server. It simplifies database setup, operation, and scaling.
  • Amazon DynamoDB: This NoSQL database service provides seamless scalability and high performance, making it ideal for applications requiring low-latency access to large datasets.
  • Amazon Redshift: Designed for data warehousing and analytics, Redshift offers columnar storage, parallel query processing, and integration with popular BI tools.

Azure Databases

  • Azure SQL Database: A managed relational database service, Azure SQL Database offers compatibility with SQL Server and provides features like built-in AI, automatic tuning, and geo-replication.
  • Azure Cosmos DB: A globally distributed NoSQL database, Cosmos DB offers high availability, low latency, and support for multiple data models, catering to globally scalable applications.
  • Azure Database for PostgreSQL/MySQL: These managed services allow users to deploy and manage PostgreSQL and MySQL databases on Azure with automated backups, scaling, and security.

Comparison

  • Relational and NoSQL Offerings: Both platforms provide managed options for relational and NoSQL databases tailored to different application requirements.
  • Scalability and Performance: Azure Cosmos DB and AWS DynamoDB provide high scalability and performance, while Azure SQL Database and AWS RDS offer managed relational options.
  • Integration and Ecosystem: Azure SQL Database seamlessly integrates with Microsoft services, and AWS databases complement AWS’s extensive service ecosystem.
  • Use Case Alignment: AWS’s Amazon RDS and Redshift might be preferred for data warehousing, while Azure’s Cosmos DB appeals to globally distributed applications.

Azure vs. AWS: Content Delivery and Networking Connectivity

Content delivery and networking connectivity are pivotal aspects of aws vs azure vs google cloud services comparison, impacting user experience and application performance. 

The comparison between Microsoft Azure and Amazon Web Services (AWS) in these domains unveils each platform’s capabilities to enhance content delivery, networking, and connectivity strategies. The following are the points to be considered: 

  • Global Reach: Azure’s vast data center presence and Amazon’s global infrastructure ensure widespread coverage, optimizing content distribution and reducing data traversal distances.
  • Networking Services: Azure Virtual Network and AWS Virtual Private Cloud (VPC) enable users to create isolated networks for enhanced security and controlled connectivity.
  • Direct Connect and ExpressRoute: AWS offers Direct Connect, while Azure provides ExpressRoute, facilitating dedicated and private network connections between on-premises infrastructure and the cloud.
  • Hybrid Connectivity: Azure Hybrid Connections and AWS Direct Connect Gateway enable seamless integration between on-premises and cloud resources, ensuring a unified networking environment.

Which is Better Azure or AWS?: Job Opportunities

Machine Learning (ML) is reshaping industries by automating tasks and uncovering insights from data. Amazon Web Services (AWS) and Microsoft Azure, two cloud computing giants, offer comprehensive ML solutions. 

This comparison delves into the ML offerings of both platforms, examining their capabilities, tools, and frameworks to empower businesses with the potential of ML-driven innovation.

  • Services Offered: AWS offers Amazon SageMaker, providing end-to-end ML workflow and AWS Deep Learning AMIs for specialized tasks. Azure’s Machine Learning service encompasses ML lifecycle management and Azure Cognitive Services for pre-built AI capabilities.
  • Ease of Use: Both platforms prioritize ease of use. AWS SageMaker’s built-in algorithms and Azure’s drag-and-drop interface streamline model development.
  • Frameworks and Libraries: AWS supports popular frameworks like TensorFlow and PyTorch. Azure integrates with Microsoft’s ML libraries and supports various open-source tools.
  • Scalability: AWS and Azure offer scalable ML resources, with AWS’s Lambda and Kubernetes Service providing auto-scaling.
  • Integration: Azure’s strength lies in integration with Microsoft tools. AWS’s ecosystem spans a broader spectrum of services.
  • Deployment and Inference: Both platforms enable model deployment, with AWS Lambda@Edge and Azure Functions for real-time inferences.
  • Cost Management: Azure provides cost analysis and forecasting tools. AWS offers cost management tools through its AWS Cost Explorer.

Azure Compared to AWS: Job Opportunities

The increasing adoption of cloud technologies has surged demand for skilled professionals in Microsoft Azure and Amazon Web Services (AWS). Azure’s integration with Microsoft’s ecosystem attracts positions in enterprises already invested in Microsoft tools. AWS’s vast service portfolio creates a demand for diverse roles across industries. Cloud architects, engineers, developers, and administrators are sought-after positions on both platforms. 

The choice between Azure and AWS job opportunities depends on alignment with existing skills, career aspirations, and the platform’s integration within the organization’s technology landscape.

Difference Between AWS and Azure Salary

Professionals skilled in Amazon Web Services (AWS) and Microsoft Azure get competitive salaries due to the soaring demand for cloud expertise. Both platforms offer diverse job opportunities, including cloud architects, engineers, developers, and administrators. 

The salaries vary based on experience, location, and role complexity. AWS’s expansive market share and Azure’s integration with Microsoft technologies contribute to the strong demand, resulting in attractive compensation packages. 

Here is an overview of the average salaries of different positions in the US in AWS and Azure:

Position AWSAzure
Software Engineer$135,337 per year$125,000 per year 
Senior Software Engineer$171,690 per year$150,140 per year
Software Architect$146,968 per year$155,019 per year
Cloud Engineer$101,887 per year$132,478 per year

As businesses migrate to the cloud, the potential for rewarding careers in AWS and Azure remains high, choosing between the two even more compelling for professionals in the field.

AWS vs. Azure: Difficulty Level

Selecting between Amazon Web Services (AWS) and Microsoft Azure involves considering the platforms’ difficulty levels. AWS offers many services and a steeper learning curve, making it suitable for experienced users seeking extensive customization. 

In contrast, Azure prioritizes integration with Microsoft products, making it more approachable for those familiar with the Microsoft ecosystem. Both platforms provide comprehensive documentation and resources for learners at different skill levels, allowing businesses to tailor their choice based on their team’s expertise and the complexity of their cloud computing needs.

AWS vs. Azure: Advantages and Disadvantages

Here are the advantages and disadvantages of AWS and Azure:

AWS Advantages and Disadvantages

Advantages

  • Market Dominance: AWS leads in market share and offers many services.
  • Extensive Ecosystem: A wide selection of services for diverse use cases.
  • Strong Global Presence: Multiple data centers globally for reliable performance.
  • Enterprise-Grade Security: Offers advanced security features and compliance.

Disadvantages

  • Learning Curve: Vastness of services might lead to a steeper learning curve.
  • Complexity: Extensive customization options can lead to complex configurations.

Azure Advantages and Disadvantages

Advantages

  • Integration: Seamless integration with Microsoft products and services.
  • Hybrid Cloud: Strong hybrid cloud capabilities for on-premises and cloud integration.
  • AI Integration: Microsoft’s AI and analytics tools enhance data-driven insights.
  • Familiarity: Preferred by organizations using Microsoft technologies.

Disadvantages

  • Learning Microsoft Ecosystem: Non-Microsoft users might find the learning curve steeper.
  • Service Variety: Although comprehensive, not as extensive as AWS’s offerings.

Conclusion

As we’ve dissected AWS and Azure’s offerings, capabilities, and advantages, it’s evident that their choice hinges on organizational requirements, existing infrastructure, and strategic goals. Whether one leans towards AWS or Azure, the ultimate aim is an educated decision that aligns with business objectives. Both platforms provide tools and assistance for efficient application deployment and management in the cloud.

As the cloud landscape evolves rapidly, staying current with the latest developments is imperative. To amplify your cloud computing expertise and propel your professional journey, explore BlackBelt Plus program. This comprehensive initiative equips you with the skills necessary to navigate the intricacies of cloud computing and beyond.

Frequently Asked Questions

Q1. Which is better, AWS or Azure?

A. Both AWS and Azure have their strengths. The choice depends on factors like integration preferences, services needed, and budget considerations.

Q2. What is the main difference between Azure and AWS?

A. One key difference between AWS and Azure is integration. Azure works well with Microsoft products. In contrast, AWS offers a broader range of services and holds extensive market share.

Q3. Which is high-paying, AWS or Azure?

A. Both AWS and Azure skills are in high demand and offer competitive salaries. The earning potential depends on various factors.

Q4. Which is more in demand, Azure or AWS?

A. Both Azure and AWS are in high demand due to the growing adoption of cloud technologies across industries.

Amazonamazon web servicesAWSAWS vs AzureAzureClouddatabaseintegrationMicrosoftMicrosoft azure
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