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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

Scalable Searching with Amazon Elasticsearch Service

Arnab M
Arnab M Last Updated : 19 May, 2022
4 min read

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

Introduction on Amazon Elasticsearch Service

Amazon Elasticsearch Service is a powerful tool that allows you to perform a number of functions. Let us examine how this powerful tool works behind the scenes. Elasticsearch acts a lot like a database and a distributed system that works like a freely available power tool to join in with other AWS services for application development. This allows you to develop on the go with time-saving techniques. Here, we will discuss a few key points about the AWS Elasticsearch service that will help you to develop faster and more effectively.

Some of the key tools and services that help developers to load a large amount of data and build solutions for users are as follows:

  • Kibana dashboard helps devs to visualize and build smoothly.

  • Elasticsearch service acts as the ad hoc search engine

  • Logstash helps to migrate data into the Elasticsearch search engine

These are inbuilt features of AWS Elasticsearch which make developing easier and more efficient. With these tools at your fingertips, you can easily create an application from scratch in no time. As we know, distributed systems require a number of maintenance type steps such as deploying new nodes, restarting clusters and maintaining the health of clusters by restarting failed nodes. However, AWS Elasticsearch is different as it provides us with the luxury of easily maintained, deployable and scalable clusters. So we no longer need to do all of the maintenance work that is required with most other distributed systems.

Nodes Provided Within Amazon Elasticsearch Service Clusters

  • Data Nodes: To store data that needs to be provided to the search engine.

  • Master Nodes: To administer the elastic search cluster.

  • Ultra-warm nodes: These are highly available nodes that store data for long periods of time. Such nodes are distributed across availability zones or AZs so that they remain accessible under all circumstances.

ES Documents

ES Documents refers to the final products that we can retrieve from Elasticsearch and save it. A document is generally a JSON object which is searchable. Under Elasticsearch, these documents are stored under indexes created by REST APIs. These documents can then be searched using their respective indexes through field matching, boolean queries, sorting and analysis. These tasks are again performed by another set of powerful REST APIs.

ES Security

AWS Elasticsearch service supports the highest level of security features to support development. Here are four main points that constitute ES Security.

  • AWS Elasticsearch service provides data encryption.

  • Node to node communication encryption

  • IAM secure authentication

  • It provides an open distro needed for the Elasticsearch security plugin for smooth and intricate access control

If you want to integrate it with the various other services of AWS, there are multiple inputs and outputs which can be used for AWS ES.

  • Amazon Kinesis Data Firehose

  • AWS Database Migration Service

  • Amazon CloudWatch Logs and output integrations with:

  • Amazon CloudWatch

  • AWS CloudTrail

The 5 input integrations mentioned above are the most important and the in-built ones which are supported as default by the AWS ES Service. We can also integrate a custom one for the AWS Services by using the IAM roles and Lambda Functions.

AWS ES service does support various types of workloads and these workloads can be used to solve many of our problems which come under various scenarios. Thus this is how Elasticsearch service can be used to solve real-world problems.

ES Workloads

Elasticsearch service supports various different workloads. Workloads help to create abstracts of the solutions which will then help you to create the solution you want. Some common workload categories are as follows:

Search workloads:

  • Searching from large data files and loading data.

  • Perform queries, adjust rankings and select from various language features.

Analytics workloads:

  • Near real-time availability of log data

  • Perform visualizations, create dashboards, set up alerting and monitoring systems

ES Cluster Configurations

Cluster configurations are extremely important as it should be tailor-made for the purposes of the project. As you configure your cluster, you need to focus on the current data load and also the data load you may receive in the future when your project is operational. Depending on the purpose of your project, you may need to splice up your data for it to be meaningful for your purposes. Now the data chunks will be ready to be represented in Elasticsearch clusters. Now there are a few factors on which the cluster config will depend. Those are as follows:

  • The count of Instances — To scale your cluster, you need to increase or decrease the number of instances in your cluster

  • The Type of Instance — Depending upon the type of instance, the cluster capacity will differ.

  • Adding Storage— You need to keep adding storage to expand and scale your ES cluster with time

  • The count of the shard — The perfect balance between your index and storage amount related to every index is required and you need to identify it.

Amazon Elasticsearch Service

ES Cons

The AWS Elasticsearch service is powerfully efficient but it also has its own set of cons and downsides. Amongst the number of useful features, ES cons also constitute a considerable premium charge to avail of all of the tools and services. The reason why the price is a con is because of its relatively expensive nature as compared to other tools and services provided by AWS. Another con which needs to be listed here is the restrictive clusters provided by AWS Elasticsearch. The paired APIs provided by AWS ES is also restrictive and less efficient when compared to other open-source models and tools. Cluster configuration and other setup tasks also constitute a huge part of the process and there are no guides or tools to help out developers who are new to the environment.

Conclusion on Amazon Elasticsearch Service

ES is a service provided and managed by AWS so it reduces the amount of effort that goes into the maintenance and care of the ES clusters. There are also a number of APIs paired with the service to help you work on different aspects. Being a service provided by AWS, it has key features that help you to integrate the Elasticsearch projects with other AWS services which is a very useful feature for developers building complex applications and projects.

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

Arnab M
Arnab M

I love to code and create new software for any purpose. I also love to play MMO and RTG games. Other hobbies include Exploring new places and restaurants and making new friends. Feel free to ping me on LinkedIn for any new ideas or same and if you need any help with any code too.

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