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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

What is AWS? Why Every Data Science Professional Should Learn Amazon Web Services

M
Mukul Taneja Last Updated : 28 Sep, 2020
6 min read

Overview

  • Amazon Web Services (AWS) is the leading cloud platform for deploying machine learning solutions
  • Every data science professional should learn how AWS works

 

Introduction

“Your machine ran out of memory.”

Sounds familiar? It certainly is for me – especially anytime I try to run a complex machine learning algorithm on my personal machine. It’s quite a frustrating experience that a lot of data science professionals feel. We don’t have the unlimited computing power of the tech behemoths – so what should we do?

This is where the power of the cloud has transformed data science. And Amazon, with its AWS offering, has conquered the data science market like nothing before.

Amazon Web Services

Cloud computing has seen tremendous growth in the past few years. Almost every organization nowadays uses cloud computing for its wide range of services. 70% of all the money spent on tech is expected to go into cloud services by the end of 2020.

Did you know that AWS’s revenue in the first quarter of 2020 was $10 billion? That’s almost twice as much as its next closest competitor! Every data science professional, from a data science to a data analyst, needs to learn AWS and how it works.

So in this article, let’s dive into what AWS is and find out why it has come at the forefront of cloud computing services.

 

Table of Contents

  1. What is Amazon Web Services (AWS)?
  2. History of Amazon Web Services
  3. Services provided by Amazon Web Services
  4. Here’s why you can’t use your local system for all of your data tasks
  5. How can Amazon Web Services help you?

 

What is Amazon Web Services (AWS)?

AWS is a cloud computing platform by Amazon that provides services such as Infrastructure as a Service (IaaS), platform as a service (PaaS), and packaged software as a service (SaaS) on a pay-as-you-go basis. It was launched in 2006 but was originally used to handle Amazon’s online retail operations.

AWS has 3 main products:

  1. EC2 (Amazon Elastic Compute Cloud):
    EC2 allows users to rent virtual machines/servers on which they run their own applications. These servers come in different operating systems and Amazon charges you based on the computing power and capacity of the server (i.e. Hard Drive capacity, CPU, Memory, etc.) and the duration the server been up
  2. Glacier
    Glacier is a low-cost online file storage web service. Amazon Glacier is designed for the long-term storage of inactive data that will not need to be quickly retrieved
  3. S3 (Amazon Simple Storage Services)
    S3 provides object storage through a web service interface, with scalability and high-speed being its boonAmazon Web Services

AWS provides its consumers with many advantages:

  • Security: AWS provides comprehensive security capabilities to assure the most demanding requirements
  • Compliance: AWS has rich controls, auditing, and broad security accreditation
  • Hybridism: It allows the building of hybrid architectures that extend the on-premises infrastructure to the cloud
  • Scalability: It allows scaling up and scaling down with ease
  • Pay-as-you-go: This means that you pay in accordance to the services you use. Useless, pay less. Use more, pay more but per-unit price goes down as you scale up

Here is an article that will help you begin your journey in using AWS:

 

History of Amazon Web Services (AWS)

AWS was initially launched in 2002 but it provided only a few services. In 2006, AWS launched its cloud products which included Amazon S3 cloud storage, SQS (Simple Queue Service), and EC2 and in doing so, marked its entry in the online core services industry.

In 2009, AWS saw the international expansion of AWS to Europe where S3 and EC2 were launched. Elastic Block Store (EBS), which provides block-level storage, and Amazon CloudFront, a content delivery network, were released and incorporated into AWS.

It provides block-level storage to use with Amazon EC2 instances. Amazon Elastic Block Store volumes are network-attached and remain independent from the life of an instance.

Over the years, a lot of services were added to the AWS platform which has made it a cost-effective and highly scalable platform. Now, AWS has its data centers all over the world including the United States, Japan, Europe, Australia, and Brazil.

AWS Global Infrastructure map

 

Services provided by Amazon Web Services

The following services are provided by AWS in the respective domains:

  1. Compute Services:
    • EC2 (Elastic Compute Cloud)
    • EKS (Elastic Container Service for Kubernetes)
    • Lambda
    • Amazon LightSail
    • Elastic Beanstalk
  2. Database Services:
    • Neptune
    • RDS
    • Aurora
    • RedShift
    • DynamoDB
    • ElastiCache
  3. Security Services:
    • KMS (Key Management Service)
    • AWS IAM (Identity and Access Management)
    • Inspector
    • WAF (Web Application Firewall)
    • Cloud Directory
    • Certificate Manager
    • Organizations
    • Shield
    • Macie
    • GuardDuty
  4. Storage Services:
    • Amazon Glacier
    • S3 (Simple Storage Service)
    • AWS Snowball
    • Elastic Block Store
  5. Migration Services:
    • Snowball
    • DMS (Database Migration Service)
    • SMS (Server Migration Service)
  6. Analytical Services:
    • Kinesis
    • QuickSight EMR (Elastic Map Reduce)
    • Data Pipeline
    • CloudSearch
    • Athena
    • ElasticSearch
  7. Management Tools:
    • CloudWatch
    • CloudFormation
    • CloudTrail
    • OpsWorks
    • Config
    • AWS Auto Scaling
  8. Messaging Services:
    • Pinpoint
    • SQS
    • SES
    • SNS

For more information on services provided by AWS, click here.

By now you would have a broad understanding of what AWS is. So now, let’s shed some light on why companies require their data scientists to know AWS.

 

Here’s why you can’t use your local system for all of your data tasks

Remember when you were just sitting idle waiting for the system to respond? Here, we highlight a list of problems that your local systems must be able to overcome:

  1. The system on which you deploy tasks has low processing power that will have a drag on your punctuality. You must have noticed this while processing huge volumes of data and I am pretty sure the thoughts of an external, centrally managed system must have crossed your mind
  2. Large data sets don’t fit into the IDE’s system memory which is required for analytics or model training. Remember when your Jupyter Notebook got stuck?
  3. It costs a lot both in terms of time and money to install and maintain your own hardware

 

How can Amazon Web Services help you?

I am sure many of you would be still wondering why you should use AWS? Why not go for something else (like Google’s GCP)? Let me answer this by giving the following benefits fo AWS:

  1. User Friendly

    AWS has a very well documented user interface which eradicates the requirement of on-site servers to meet the IT demands. This eases up the deployment of programs, software from time to time. AWS meets your every need.

  2. Diverse Tools

    Earlier in this article, we saw what a diverse range of services AWS has to offer. It’s the all in one solution for your IT and cloud requisites considering its efficiency.

  3.  Computing Capacity

    You don’t need to worry about whether large datasets will fit into your IDE’s system memory or not.

  4. Infrastructure

    The AWS Global Cloud Infrastructure is the most extensive, and reliable cloud platform, offering over 175 fully-featured services from data centers globally. Whether you need to deploy your application workloads across the globe in a single click, or you want to build and deploy specific applications closer to your end-users with single-digit millisecond latency, AWS provides you the cloud infrastructure where and when you need it easily.

  5. Pricing

I sense this will act as the most convincing points! AWS is one of the cheapest platforms for cloud servicing. This is really useful for small businesses to function and grow without allocating much working capital on servers.

CIPS_MQ_transparent

2020 Gartner Magic Quadrant for Cloud Infrastructure and Platform Services

 

Why do companies emphasize on AWS knowledge for their data scientists?

Whichever firm you work for, cloud infrastructure will become an important part of your daily data science regime because companies have become more inclined towards cloud computing for solutions.

According to a report from Indeed.com, AWS rose from a 2.7% share in tech skills in 2014 to 14.2% in 2019. That’s a 418% change!

This is because of the pricing model on which AWS works. AWS works on a pay-as-you-go model and charges on either a per-hour or a per-second basis. It also provides an option to reserve a specific amount of computing capacity at discounted rates.

Additionally, AWS keeps in mind the prospective consumers who can’t afford its services. For them, it provides the AWS Free Tier service which allows them to gain hands-on experience with AWS services absolutely free.

All businesses, whether big or small, want to save costs. Small companies save costs of buying servers and conglomerates gain authenticity and productivity. AWS services are also very powerful. On one hand, where it takes days to set up a Hadoop cluster with Spark, AWS does it within a few minutes.

 

End Notes

In today’s competitive world, having hands-on experience with cloud services like AWS gives a great lead in the data science race. AWS is now very popular among businesses and your experience with such cloud computing platforms highlights your skills during the recruitment process.

Here are some additional resources that you should look into:

I hope this article serves as a solid argument supporting why cloud computing is necessary for data scientists. Please use the comment section below if any thoughts to share or general queries.

amazon web servicesAWSAWS data scienceAWS machine learningCloud Computing
M
Mukul Taneja

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Responses From Readers

Clear

Colin Goyette
Colin Goyette

I cannot disagree with this opinion article more. People spend their entire careers studying and administering systems of IT infrastructure. Others spend their entire careers studying mathematics, statistical methods, theory of computer science, and areas of domain expertise. Arguing that every data science professional needs to also have professional level sysadmin or devops skillsets implies that building valuable and differentiated models also requires operating and maintaining the underlying machinery for the tasks that they alone are qualified for: studying data closely, performing experiments efficiently, and documenting their research clearly. Requiring these skillsets in parallel implies cultures of heroism, overwork, and a lack of collaborative culture. Simply put: let scientists be scientists.

123

Praveen Kumar M
Praveen Kumar M

Thanks, gives a quick background of AWS.

123

Jagannath Bulusu
Jagannath Bulusu

Good article to understand AWS overview.

123

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