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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 Route 53 – The Efficient DNS Solution

Ansaba
Ansaba Last Updated : 07 Dec, 2022
5 min read

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

AWS Route 53
Source: https://www.cdnoverview.com/multicdn/amazon-route53/

Introduction

Nowadays, a lot of data is being generated and consumed, resulting in a huge amount of internet traffic exponentially across the globe. So it is very important to manage the traffic and serve users or customers better and more efficiently. Also, businesses and other organizations that run machines in the cloud using AWS need an AWS DNS solution to correctly translate user requests into IP addresses while adapting to cloud changes and quickly generating them for DNS clients. Hence, through AWS Route 53, Amazon serves its customers better by managing the domains and routing Internet traffic. AWS Route 53 is Amazon’s official DNS solution to manage and maintain domains, and thereby it helps in boosting business reliably and cost-effectively.

What is AWS Route 53?

AWS Route 53 or Amazon Route 53 is a reliable and cost-effective Domain Name Web Service. It provides a distributed DNS service where a domain name system translates human-readable domain names, such as www.amazon.com, to a machine-readable IP address, such as 192.0.2.44. It can route users’ Internet requests to any application — whether it’s hosted on AWS or elsewhere. Amazon Route 53 can be used either to configure DNS health checks to route traffic to healthy endpoints or to monitor the health of your application and its endpoints independently. It is an Authoritative DNS service that stores the most recent and accurate DNS records for a domain and contains information about mapping IP addresses to domain names. Amazon Route 53 can connect the request of users to other AWS services such as Amazon EC2 instances, Amazon S3 buckets, or Elastic Load Balancing load balancers.

What is the Significance of the Name ‘AWS Route 53’?

AWS Route 53 takes its name from TCP or UDP port 53, where DNS server requests are addressed. The term ‘Route’ is basically for routing traffic to Amazon DNS.

Functions of Route 53

AWS Route 53

Source: https://www.hava.io/blog/what-is-amazon-route-53

  • DNS Management:
    With Amazon Route 53, users can create and manage public DNS records. Route 53 supports end-to-end DNS resolution over IPv6. Recursive DNS resolvers on IPv6 networks can use IPv4 or IPv6 transport to send DNS queries to Amazon Route 53. Also, Route 53 helps create DNS records for a new domain or transfer DNS records for an existing domain.
  • Monitoring:
    Route 53 can monitor the health and performance of users’ resources or applications and send traffic accordingly through healthy resources only. Health check generates CloudWatch metrics that trigger AWS Lambda functions to perform appropriate corrective actions.
  • Traffic Management:
    Through Route 53 Traffic Flow service, users can easily manage traffic globally through various routine types such as Latency Based Routing, Weighted Round Robin, Geo DNS, etc. Also, if the user has any existing domain in another platform, it is possible to switch the same domain into route 53, routing the traffic for the user’s domains.

Routing Policies

routing policy

Source: https://www.stormit.cloud/blog/amazon-route-53/

Route 53 offers several policies for efficient DNS requests. While creating the records, a routing policy has to be chosen. It decides how Route 53 responds to DNS queries. Different routing policies are:

 

 Routing Policies Definition Use Case Key Feature
 

Simple routing policy 

 Most basic routing types are where a record can be associated with one or more IP addresses. Record creation in a private hosted zone It can contain multiple values, but it returns only one resource.

Weighted routing policy		 Route traffic to multiple resources in specified proportions when the traffic must be split between the resources based on some mentioned weights. New deployments or releases testing new versions Users can reply to a single DNS query with different IP addresses
Latency routing policy If the user has resources in multiple AWS regions, the latency routing policy routes traffic to the region that provides the best latency. Record creation in a private hosted zone Works according to the latency measurements performed over a period of time, and the changes are reflected in measurements.
Failover routing policy Defines a record to be primary and a different record to be secondary. The failover routing policy sends all traffic to the server set as primary as long as that server is healthy. If health checks determine it’s failing, traffic will be diverted to a designated secondary or backup resource.
 Used if resources have to be available for the majority of the time  Applicable only for Public hosted zones.

Companies Using AWS Route 53 Service

AWS Route 53

Source: https://ourtechplanet.com/amazon-web-services-aws-the-market-leader/famous-customers-using-aws-route53-services/

Key Features of Route 53

  • AWS service integration
    AWS Route 53 can be integrated with other AWS services such as Cloud Front, ELB, S3, etc. ELB integration helps in better load balancing. Other benefits associated with integration are routing traffic easily to static websites hosted on S3 and generating custom domains for Cloud Front URLs.
  • Scalable
    Route 53 automatically handles large volume queries without the user’s interaction.
  • Geographical DNS
    It helps the user to balance the load across different endpoints based on the geographic location of end users from which the request originates.
  • Secure
    Through Integration with AWS Identity and Access Management (IAM), there is complete control over every user within the AWS account by granting them unique credentials and permissions.

Why Should you Consider Migrating to AWS Route 53?

  • As a pay-as-you-go service, the user will be charged for the number of hosted zones they create and the number of requests routed.
  • Compatible with IPv6.
  • Able to route Internet traffic for domains registered with another domain registrar.
  • It has APIs that help easily create and manage user domain DNS records.
  • Able to handle large query volumes through autoscaling.
  • Intelligent traffic routing based proximity, the health of endpoints, and latency.
  • Route 53’s servers are distributed all over the world.
  • Has a simple web-services interface that helps users get started in much less time.

Conclusion

Route 53 is designed for various businesses and developers to give them a flexible, cost-effective, and secure way of routing. Domain registration and creation are much simplified with AWS Route 53. Also, DNS settings for domains are automatically configured by this service.

Key Takeaways:

  • AWS Route 53 is a reliable and cost-effective Domain Name Web Service.
  • It handles domain registration and provides worldwide distributed DNS service.
  • Route 53 helps to route traffic based on multiple criteria, such as endpoint health, geographic location, and latency.
  • Main functions include DNS management, Monitoring, and Traffic management.
  • Routing policies decide how Route 53 responds to queries.
  • Different routing policies associated with Route 53 include simple routing policy, weighted routing policy, latency routing policy, failover routing policy, etc.
  • AWS Route 53 can be integrated with other AWS services such as Cloud Front, ELB and S3

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

Amazonamazon awsAWSAWS cloud searchAWS data scienceblogathon
Ansaba
Ansaba

I'm a cloud enthusiast and have done graduation in computer science. My skills include C, C++, Php, Python, Cloud services(AWS, GCP), shell scripting, MySQL, etc. I have hands-on experience and have done several projects and certifications in areas of Python, Php, MySQL, Cloud (GCP, AWS), and C++

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