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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

Containerization World with Docker and Kubernetes for Beginners

Leandro
Leandro Last Updated : 08 Sep, 2022
5 min read

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

Introduction

We go back in time to discuss the history of containers and virtualization. It’s important to bear in mind that the containers can exist because of the possibility of the machines being virtualized. I will reinforce this because we talk about the differences between the host in virtualized vs. container environment. Therefore, it’s important to know that both are use cases that can be used for each project type.

A Short History

The history beginnings with the technology that introduces virtualization, where the project V-Server has developed by Jacques Gélinas in the year 2002, these make it possible to run several Linux servers in a box with independence and security. In the same year, the Linux Kernel received an important update that years later will have gone made it possible to build containers how we know them today. This update introduces namespaces. With these, it’s possible to run a process isolating these from others using global resources, and containers use these in your core functionality.

To understand containers, we will explain the basics of how this architecture works and the differences between the virtualized machines.

Virtual Machines

The virtualized machine is a layer between the hardware and the emulated Operational System that run applications. We will use web apps; for example, in the image below, we can see how this architecture works.

virtual machines
Virtualization Model

This model is used by hosts to up services to be accessed by sharing resources of a server between various virtualized O.S in unique machine hardware. In the image above, we can see an important component responsible for providing installation of different O.S over the host base O.S; this component is the hypervisor.

docker model
Virtualization Hypervisor

This technology provides many benefits to infrastructure costs with hardware resource allocation optimization and provides the environment to able containers possible.

Containers with Docker

The containers you see above can run isolated processes on the same machine; virtualized containers use fewer resources to up an application environment because the container engine runs over the O.S layer and provides isolation of many resources instead of using the entire O.S environmental resources. An example of architecture is the docker, which you can see in the image below.

docker model | Kubernetes
Containerization Docker Model

The docker container has many things we can mention, but the docker image is the main component; these images can be deployed to a docker repository of artifacts on DockerHub or private Enterprise.

Kubernetes

Docker needs an orchestrator to manage the images, networks, and many other features. Here, we introduce the Kubernetes or K8s(eight letters between K and S). The Kubernetes has the docker into your architecture performing an easy-to-use way of containers and services that containerization provides us. In the image above, we can see a structure of K8s.

Kubernetes
K8s Architecture

We can see that k8s incorporate docker in a layer responsible for the containers running into a Pod. The Pod is a set of one or more Linux containers and is the basic unit of K8s.

Controlling Nodes of Kubernetes

To understand the power of Kubernetes, we need to understand how it controls the big ecosystem of nodes providing a powerful environment for teams to build fault tolerance for your microservices, databases, and applications.
In the last section, we represent the k8s architecture with a figure to represent where docker is present on this system; this has an important agent that we need to figure out and call, in this case, Master Node; this can be referred to more recently on k8s documentation as Control Plane Node. Their role remains the same, providing the running environment to control plane agents to manage the state of the Kubernetes cluster, providing control, scheduling, and interface for access for the k8s node’s resources, therefore providing your maintenance of stability and High Availability across the easy way of the store the current state of the resources on the cluster.

Main Components in Kubernetes Architecture

We will introduce in this section any components of Kubernetes that make the magic for us by providing a developer-friendly system for containerizing complex applications.
  • API Server
    Is responsible for providing an interface across a REST API for accessing the cluster resources and operations for different role levels, i.e. developers, administrators, and other agents.
  • Controller Managers
    Watch components on the Kubernetes cluster to provide the desired state, acting as manager of nodes states during k8s cluster operation.
  • etcd
    It is a key-value data store used to save Kubernetes state. This is not a simple feature once it provides the data transfer between distributed clusters, building the desired High Availability for the systems.
  • Scheduler
    The Kube-scheduler has an important role in scheduling nodes into a Kubernetes container making decisions based on the cluster state data stored.
Kubernetes has many other components, but these are the main ones, and you can see about the others if you desire on Kubernetes documentation.

Conclusion

Kubernetes architecture solves many of the problems with some concepts that improve the best of the containerization fundamentals that docker efficiently implemented. It helps the usage of computational resources smartly by providing short-size images that allow building robust systems with low memory and CPU different than we had on virtualized machines.

This tutorial aimed to introduce the concept of containerization, and we can conclude that it is not hard to understand the background of Kubernetes and docker. Now you can check the references and explore more and learn how to migrate or build your systems for the containerized side that will turn it fast to scale and distribute for your customers.

In this tutorial, you learned:

  • The introduction of the namespace on Linux Kernel that allows the build of Docker and other container-based systems;
  • The contrast between virtualized and containerized approaches;
  • Basic of Docker architecture layers;
  • Basic components of the Kubernetes architecture;
The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.
Leandro
Leandro

DevOps Automation Engineer and Researcher in A.I field. My purpose is improve infrastructure automation tasks using A.I approach to optimize costs and performance inside cloud native environments.
Also has experience with financial prediction and missing data handling.

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