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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

Developing and Deploying Applications Using AWS Outposts

Swapnil Vishwakarma
Swapnil Vishwakarma Last Updated : 09 Jan, 2023
5 min read

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

Introduction

Introduction to AWS Outposts
Source – pexels.com

AWS Outposts is a fully managed service that allows users to extend the AWS infrastructure to their premises. This means that users can build and run applications on their premises using the same programming interfaces as in AWS Regions. This allows for lower latency and local data processing.

An Outpost is a pool of AWS compute and storage capacity installed at a customer site. AWS operates, monitors, and manages this capacity as part of an AWS Region. Users can create subnets on their Outpost and specify them when creating AWS resources like EC2 instances, EBS volumes, ECS clusters, and RDS instances. This allows instances in Outpost subnets to communicate with other instances in the AWS Region using private IP addresses, all in the same VPC.

AWS Outposts provide local access to AWS-managed infrastructure and allow users to create various resources on their Outpost, including Amazon EC2 instances, Amazon EBS volumes, and Amazon RDS instances. This allows users to support low-latency workloads that must be run in close proximity to on-premises data and applications.

Overall, AWS Outposts provide a convenient way for users to access AWS services and resources on their premises while still being able to take advantage of the benefits of the AWS cloud.

How Does AWS Outpost Work?

Way AWS Outposts work
Source – Unsplash

AWS Outposts are fully managed, native AWS hardware that can run AWS services on-premises. AWS Outposts enable native AWS services, infrastructure, and operational models to be delivered to practically any data center, co-location facility, or on-premises solution.

The following are some of the prime elements of AWS Outposts and how they function:

  • Network components: AWS Outposts come with network switches and routers managed by AWS. These network components connect your on-premises resources to the rest of the AWS infrastructure.
  • VPCs and subnets: Like in the cloud, AWS Outposts use virtual private clouds (VPCs) and subnets to isolate your on-premises resources logically. You can create multiple VPCs and subnets on your AWS Outposts to suit your needs.
  • Routing: AWS Outposts come with a default route table to route traffic between your on-premises resources and the rest of the AWS infrastructure. Additionally, you can create and manage the custom route tables on your AWS Outposts to control how traffic is routed.
  • DNS: AWS Outposts come with a Domain Name System (DNS) service that can be used to resolve domain names to IP addresses. This allows your on-premises resources to communicate using domain names instead of IP addresses with other AWS services.
  • Service link: The service link is a dedicated network connection between your AWS Outposts and the rest of the AWS infrastructure. This connection transmits data between your on-premises resources and AWS services.
  • Local gateways are virtual appliances that run on your AWS Outposts and act as gateways to other AWS services. These gateways provide access to services like Amazon S3 and Amazon EBS, allowing you to store and manage data on your AWS Outposts.
  • Local network interfaces: Local network interfaces are virtual network interfaces that are attached to your AWS Outposts. These interfaces can connect your on-premises resources to other AWS services, like Amazon EC2.

Overall, AWS Outposts provide a seamless and consistent experience for running AWS services on-premises, allowing you to use the same tools and APIs that you use in the cloud.

Security in AWS Outposts

Security in AWS Outposts
Source – Unsplash

AWS Outposts is a reliable platform designed to meet the needs of the most security-sensitive companies. Security is a shared responsibility between AWS and its customers, and the shared responsibility model also applies to AWS Outposts. This means that AWS is responsible for protecting the infrastructure that runs the platform, including the global network and data centers. At the same time, customers are responsible for protecting their data and ensuring compliance with applicable laws and regulations.

To help customers protect their data, AWS suggests using Amazon IAM to manage access to AWS services and resources. IAM allows customers to create and manage individual user accounts, each with its unique credentials and permissions. This helps to prevent unauthorized access to sensitive data and ensures that only authorized users can perform specific tasks in the AWS environment.

In addition to security measures at the infrastructure level, AWS Outposts even provide encryption for data in transit and at rest. Amazon EBS encryption is available for Outpost racks to encrypt EBS volumes and snapshots using AWS KMS keys. For Outpost servers, the Amazon EC2 instance store is encrypted by default. This ensures that data is protected while it is being transmitted between the Outpost and its associated AWS Region, as well as while it is stored on the platform.

Finally, when customers stop or terminate EC2 instances on AWS Outposts, the memory allocated to those instances is scrubbed (set to zero) by the hypervisor before it is allocated to a new instance. This helps prevent any sensitive data from being inadvertently accessed by future platform users.

Overall, AWS Outposts provides a secure and reliable platform for customers to run their applications and workloads. By following best practices for data protection and access management, customers can ensure that their data remains secure on the platform.

Use Cases of AWS Outposts

There are several use cases for AWS Outposts, including the following:

  1. Disaster recovery: By running AWS services on-premises with Outposts, you can use the cloud for disaster recovery without moving your data and applications off-site. This can provide a fast and reliable way to recover from disasters like power outages, network failures, and natural disasters.
  2. Edge computing: AWS Outposts can be used for edge computing, which involves running workloads at the edge of the network, closer to users and devices. This can be useful for applications that require low latencies and high-speed data transfer, like real-time analytics and internet of things (IoT) applications.
  3. Compliance and regulatory requirements: By running AWS services on-premises with Outposts, you can maintain control over your data and applications, which can be helpful for companies that need to comply with strict data privacy and security regulations.
  4. Hybrid cloud: AWS Outposts can be used as part of a hybrid cloud strategy, allowing you to run some workloads on-premises and others in the cloud. This can provide flexibility and scalability while maintaining control over your data and applications.
  5. Cost savings: By running AWS services on-premises with Outposts, you can save on data transfer costs and reduce the need for extra hardware and infrastructure. This can result in notable cost savings for your company.

Conclusion

AWS Outposts is a fully managed service that allows users to extend the AWS infrastructure to their premises. This means that users can build and run applications on their premises using the same programming interfaces as in AWS Regions. This allows for lower latency and local data processing.

  • AWS Outposts are fully managed, native AWS hardware that can run AWS services on-premises.
  • AWS Outposts bring native AWS services, infrastructure, and operating models to virtually any data center, co-location space, or on-premises facility.
  • AWS Outposts provide a seamless and consistent experience for running AWS services on-premises.
  • Security is a shared responsibility between AWS and its customers for AWS Outposts. AWS is responsible for protecting the infrastructure, and customers are responsible for protecting their data and ensuring compliance with relevant regulations.
  • AWS Outposts come with various network components and services, like network switches and routers, VPCs and subnets, a default route table, and a DNS service.

Overall, AWS Outposts provide a convenient way for users to access AWS services and resources on their premises while still using the AWS cloud’s benefits.

Thanks for Reading!🤗

If you liked this blog, consider following me on Analytics Vidhya, Medium, GitHub, and LinkedIn.

 

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

AWSblogathonCloud ComputingData Engineering
Swapnil Vishwakarma
Swapnil Vishwakarma

Hello there! 👋🏻 My name is Swapnil Vishwakarma, and I'm delighted to meet you! 🏄‍♂️

I've had some fantastic experiences in my journey so far! I worked as a Data Science Intern at a start-up called Data Glacier, where I had the opportunity to delve into the fascinating world of data. I also had the chance to be a Python Developer Intern at Infigon Futures, where I honed my programming skills. Additionally, I worked as a research assistant at my college, focusing on exciting applications of Artificial Intelligence. ⚗️👨‍🔬

During the lockdown, I discovered my passion for Machine Learning, and I eagerly pursued a course on Machine Learning offered by Stanford University through Coursera. Completing that course empowered me to apply my newfound knowledge in real-world settings through internships. Currently, I'm proud to be an AWS Community Builder, where I actively engage with the AWS community, share knowledge, and stay up to date with the latest advancements in cloud computing.

Aside from my professional endeavors, I have a few hobbies that bring me joy. I love swaying to the beats of Punjabi songs, as they uplift my spirits and fill me with energy! 🎵 I also find solace in sketching and enjoy immersing myself in captivating books, although I wouldn't consider myself a bookworm. 🐛

Feel free to ask me anything or engage in a friendly conversation! I'm here to assist you in English. 😊

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