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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 Storage: Cost Optimization Principles

j
jayachandra Last Updated : 29 Oct, 2022
4 min read

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

AWS Storage

Introduction

Data is the most crucial aspect contributing to the business’s success. Organizations are collecting data at an alarming pace to analyze and derive insights for business enhancements.

The abundant requirement for data collection made cloud data storage an unavoidable option concerning the offerings like performance, availability, durability, security, and cost.

Top cloud provider AWS delivers best-in-class service for storage, serving object-level storage of phenomenal features with upscaling capabilities and support for all existing formats.

Companies and architects make informed decisions and refactor into calculated designs and decisions to move the data between different storage classes reducing costs. This post colors on AWS storage classes, what they offer, and how to pick among them to minimize the overall costs.

The Problem

Although storing data over the cloud reduces overall costs. At an organizational level, the amount and the scale of data will erupt.

Overtime requirements will change, and the data which seems valid and crucial will lose its importance / the business teams will focus their analysis on the new data, leading to infrequent access to existing data.

The Approach

An optimal approach to avoid this problem would be to move the data to a different storage class or delete it. This Is the path most teams would take as the overall complexity is comparatively less.

AWS storage classes

  1. S3 Standard
  2. S3 Standard Infrequent Access (IA)
  3. S3 Standard onezone Infrequent Access (IA)
  4. Intelligent tiering
  5. AWS Glacier
  6. AWS Glacier Deep Archive
Standard S3 is the most trusted and elaborate service wrt data availability, latency, and security. But the cost of the standard class is high compared to other storage classes because the other storage class has limitations regarding latency and durability.

The Solution: Adopting S3 Lifecycle Configuration

AWS has a feature: Lifecycle configurations to move/remove the data onto requirement-specific storage classes for cost-cutting.
The universal truth is that AWS S3 stores data as objects in a Flat plane. Lifecycle configurations are governing rules for defining actions on a group of an S3 object and how to handle them.
There are two categories of actions:
  1. Transition actions: defines the rules to migrate the data from the S3 standard to a requirement-specific storage class.
  2. Expiration actions: defines the rules to delete the objects after attaining a specific threshold limit.
Let us understand when to use which storage class,
The most advanced storage class is Intelligent tiering. This class is an excellent choice to cut costs when the business is unaware of the data access frequency and scale. The data/object movement occurs at a granular level between different access tiers while a change in access patterns.

When the development team is testing a POC or use case and writing mock or test data to a general bucket. The job is collecting the stats and stderr logs for analysis in the event of failure. Objects are not crucial or add value to the business, as their access is strictly required to analyze and recover from a crash or failure. Access is infrequent here. Leveraging the S3 Infrequent access storage class serves the purpose here.

When the business wants to isolate the data within a specified region for x reason, using S3 one zone Infrequent Access (IA) serves the purpose. This storage class maintains and replicates the data in one of the specified zones. The storage costs are 20% less than the standard or IA storage class.

When the data is not relevant wrt current trends or the business wants to back up the crucial data. Data archival comes into the picture. Archiving data implies it is needed only on a conditional basis in the future. S3 glacier is a storage class that offers values to store data at a petabytes scale. S3 glacier and glacier’s deep archives differ in latency and data retrieval standards. Choosing among them is again purely business purpose centric. S3 Glacier should be the final option in your optimization plan, as Glacier/Deep Archive extracts a heavy toll of latency when considering data retrieval. We can see an overhead of three to 12 hours of wait time to read the data.

Lifecycle Rule:

 

AWS Storage

The Rule comprises an XML policy that defines the actions on storage objects throughout the lifecycle.

Storage Lifecycles are accessible and managed from AWS console / AWS API / Amazon CLI. From the two categories of Lifecycle actions, transition action is composed of PUT and GET definitions, while expiration action has a DELETE definition. The request handling triggers from S3 API irrespective of the offering we choose.
The overall definition skeleton includes XML tags that act as identifiers and modifiers of S3 objects.
Example:
    
      General Example
       
        AWS Optimizations/ 
       
      Enabled 
       
        30 
      S3 Glacier Deep Archive 
       
        2000
In the following example, the configuration allows Rule as the main element for specifying the overall specifications. Flagging the buckets using tags can contribute heavily to the management of the object. The configuration provides a rule flag for handling tags. When we mark buckets with tags, we can filter and govern all the buckets falling under a specific tag/category.
Implementing best practices and proven techniques, storage, and maintenance overhead can be boosted exponentially with the added advantage of reduced organizational costs.

Conclusion

Amazon S3 is the most optimal cloud storage service leading in the market. Becoming cloud agnostic has become one of the organizational targets nowadays. Leveraging cloud services can seem straightforward, but as the business grows and requirements change/extend, cloud architects need to redesign or re-architect the infrastructure as needed.
This post showcases different AWS storage classes and offers for storage cost optimizations.
  1. We have seen how unmanaged business requirements can result in unwanted object accumulation and how this leads to organizations ending up with high operational costs if failed to clean up.
  2. The post highlights how to automate the overall durability, security, and cost can be optimized.
  3. We have seen different approaches and how to set S3 Lifecycle configurations.

Enhancing the storage policies and classes extends organizational resiliency for optimizing costs and maintenance. We highlighted different S3 storage classes and when to leverage them. The instructions can greatly impact your storage operations and cut down your overall financial burden.

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

AWSAWS data scienceblogathonData Engineering
j
jayachandra

Cloud Data Engineer Love building Functional Backends/Distributed Systems/Streaming Apps / Microservices.

Holds Expertise in Scala, Python, Apache Kafka, Apache Spark, AKKA, ZIO, SQL, NoSQL, AWS, GCP, and K8s.

Transitioning to become an expert in cloud Dataops and MLOps.

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