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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

Top 25 Docker Use Cases

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 16 Jun, 2024
9 min read

Introduction

Docker is a platform that enables developers to package applications and their dependencies into lightweight, portable containers. They have revolutionized the way software is developed, tested, and deployed across various industries. In this article, we will explore 25 different use cases of Docker across various industries, highlighting some real-world examples. We will also discuss the benefits of using Docker, when to use it, and when not to.

Learn More: Docker Tutorial: Step-by-Step Tutorial for Beginners

Top 25 Docker Use Cases

Overview

  • Understand what Docker is.
  • Get to know the wide range of use cases of Docker across various industries.
  • Gain knowledge about how some companies have used Docker for real-world applications.
  • Learn the benefits of using Docker containers and know when to and when not to use Docker.

Table of Contents

What is Docker?

Before diving into specific use cases, let’s first understand what Docker is. Docker is a powerful platform-as-a-service (PaaS) that enables developers to build, deploy, and run applications in containers. These containers package an application along with its dependencies and configurations into a single executable unit. This way, it ensures the application’s consistency across various computing environments. Moreover, these containers are lightweight and portable, making them easier to collaboratively work on. Hence, Docker is widely used for its ability to streamline development workflows, improve resource utilization, and enhance scalability.

Know More: Introduction to Docker | DataHour by Pramod Singh

Use Cases of Docker Containers

Docker containers offer versatile solutions for enhancing efficiency, scalability, security, and innovation across various industries. Organizations in healthcare, finance, eCommerce, education, and more, use Docker’s capabilities to optimize their IT operations and deliver robust services. Here are 25 diverse use cases of Docker containers for you to explore.

Use Cases of Docker Containers

Development and Testing Efficiency

  1. Standardized Development Environments

    Docker ensures that developers work in uniform and consistent environments across different systems. This reduces compatibility issues and configuration drift during application portability. A notable real-world example of this is Alibaba Cloud, which uses Docker to standardize development setups across teams.
  2. Fast Onboarding of New Developers

    Docker containers require simple setup procedures. So it’s easier for new team members to quickly set up their development environment while onboarding. For this reason, when Netflix brought in new developers, it used Docker. This accelerated the company’s setup process and allowed the new members to start contributing faster.
  3. Continuous Integration and Continuous Deployment (CI/CD)

    Docker facilitates automated testing and deployment pipelines. It streamlines the processes enabling rapid and reliable software delivery. How this helps can be seen at Carnival Corporation. The company uses Docker for CI/CD pipelines on its MedallionClass program. This allows Carnival to seamlessly deliver continuous updates and improvements to its cruise ship services.

Learn More: End-to-End Guide to Docker for Aspiring Data Engineers

Infrastructure Optimization

infrastructure optimization
  1. Server Consolidation

    Docker containers enable efficient usage of server resources by integrating multiple applications onto fewer physical servers. Companies like Knowis AG leverage Docker to scale their banking solutions as it helps them efficiently handle varying workloads by consolidating server resources.
  2. Resource Efficiency

    Docker keeps applications lightweight and minimizes overheads by efficiently managing resources. This is why NIH (National Institutes of Health) employs Docker for the resource-efficient deployment of imaging software across its hospitals. By doing so, NIH optimizes resource allocation while ensuring consistent performance in medical imaging applications.
  3. Isolated Development Environments

    Developers can isolate their applications and dependencies using Docker containers. This helps to avoid conflicts with other applications. For instance, Wiley Education Services (WES) uses Docker to isolate and manage various development environments for its educational platforms. Docker helps WES to develop, test, and deploy educational services without conflicts, ensuring reliability for online learning experiences.

Scalability and Flexibility

  1. Microservices Architecture

    Docker is very useful when it comes to microservices-based architectures. The platform allows applications to be broken down into smaller, manageable services, which can then be scaled independently. In the case of Netflix, for instance, Docker has enhanced flexibility and scalability through the independent deployment of microservices.
  2. Elastic Scalability

    Docker containers can dynamically scale applications based on demand. This ensures scalability without downtime. This is why Alibaba Cloud uses Docker for elastic scalability in its eCommerce operations. It helps the company scale by adding more instances of an application, based on demand. This ensures optimal performance during high-traffic periods without any downtime.
  3. Multi-Cloud Deployments

    One of the best features of Docker is its multi-cloud deployment and portability support. It enables applications to run seamlessly across different cloud providers, providing flexibility and avoiding vendor lock-in. Carnival Corporation leverages this feature to deploy containers across hybrid cloud infrastructures for its cruise ship services.
multi-cloud deployment

DevOps Practices

  1. DevOps Automation

    Docker enables the collaboration between development and operations teams through standardized containers. This quickens the adoption process of DevOps practices. Due to this reason, companies like Knowis AG use Docker to automate the provisioning and deployment of their infrastructure.
  2. Infrastructure as Code (IaC)

    Docker containers can be defined and managed as code. This facilitates consistent infrastructure deployment and configuration. Netflix incorporates Docker containers into its infrastructure as code by defining infrastructure requirements in Dockerfiles. This helps it achieve consistent and repeatable deployments, enhancing operational efficiency and reliability.
  3. Version Control and Rollback

    Docker images can be versioned, allowing teams to easily roll back to previous versions in case of issues or bugs. Carnival Corporation utilizes Docker for version-controlled deployments, as it minimizes downtime and ensures service continuity on cruise ships.

Security and Compliance

  1. Container Security

    Docker provides built-in security features such as isolation and resource limitations. This reduces the attack surface of applications, making them safer. Docker provides enhanced container security services for companies like Netflix to mitigate risks and protect their sensitive data.
  2. Compliance Requirements

    Docker containers can help organizations meet regulatory compliance by providing a consistent and auditable environment for applications. Alibaba Cloud, for instance, uses Docker to meet regulatory compliance in its fintech operations.

Data Management

Data management using Docker
  1. Data Volumes and Persistence

    Docker is capable of managing data volumes separately from containers, ensuring data persistence and easy backup. This is why Carnival Corporation uses Docker for managing data on its cruise ships. It also provides easy backup and restoration of critical operational data in maritime environments.
  2. Database Containers

    Docker containers are ideal for running database instances. They provide flexibility and isolation for database management systems. Knowis AG uses Dockeized databases to run database instances in scalable banking solutions and manage its database workloads efficiently.

Disaster Recovery

  1. Disaster Recovery Planning

    Docker containers simplify disaster recovery planning by enabling easy replication and restoration of applications and their dependencies. This helps companies like Carnival Corporation to ensure high availability and rapid recovery in case of system failures or data breaches.
  2. High Availability

    Docker has the ability to replicate containers across multiple hosts. This ensures high availability of applications and minimizes downtime. Netflix uses Docker for this reason, to achieve high availability in media streaming services. Docker containers facilitate load balancing and automatic failover, ensuring uninterrupted service delivery to global audiences.

Industry-Specific Applications

Industry-specific applications of Docker platform
  1. Healthcare

    Docker is used in healthcare for deploying imaging software and integrating AI tools. It enhances diagnostic capabilities across medical institutions like NIH. Docker containers enable NIH to deploy and test new imaging technologies efficiently across its network of hospitals.
  2. Finance

    Docker is widely used to implement scalable and secure applications in the fintech industry. The platform ensures reliable performance, even under high transaction volumes. Docker helps firms like Knowis AG with scalable digital banking solutions, through workload balancing and secure transaction processing. Moreover, it also helps in complying with regulatory requirements.
  3. E-commerce

    Docker containers enable rapid deployment and scaling of eCommerce applications, ensuring high performance, even during peak shopping seasons. Hence, e-commerce platforms like Alibaba use Docker to run scalable websites while ensuring high performance and reliability in online retail operations.
  4. Education

    Docker containers help in streamlining the deployment of educational services. They provide a scalable and reliable infrastructure for online learning environments. Hence, e-learning companies like Wiley Education Services (WES) use Docker to develop and launch their online educational platforms.

Innovation and Experimentation

Innovation and experimentation
  1. Innovation Labs

    Companies often set up Docker-based innovation labs to experiment with new technologies. This is because Docker containers provide isolated environments for innovation. It enables teams to build prototypes and test new ideas without impacting production systems.
  2. Testing and Prototyping

    Docker containers provide a lightweight environment for the rapid testing and deployment of new features and prototypes. Netflix uses Docker to accelerate its innovation cycle by quickly iterating and delivering new functions to its streaming platform.

Cost Efficiency

  1. Resource Optimization

    The Docker platform optimizes resource usage and reduces infrastructure costs. It helps organizations achieve cost efficiency without compromising on performance. For instance, Docker’s efficient resource management helps Alibaba Cloud maintain its high-performance cloud-based services at low costs.

Benefits of Using Docker

For a platform that has so many diverse and versatile use cases, here are some of the benefits of using Docker:

  1. Portability: Docker containers can run on any system with Docker installed, ensuring applications behave the same way across different environments.
  2. Consistency: Docker eliminates the “it works on my machine” problem by providing a consistent environment for development, testing, and production.
  3. Resource Efficiency: Docker containers share the host OS kernel, making them more lightweight and efficient than traditional virtual machines.
  4. Isolation: Each container operates independently, ensuring applications and their dependencies are isolated from each other.
  5. Scalability: Docker makes it easy to scale applications horizontally by running multiple container instances across a cluster.
  6. Faster Deployment: Containers can be created and destroyed quickly, allowing for rapid deployment and scaling.
  7. Improved CI/CD: Docker integrates seamlessly with CI/CD pipelines, enabling automated testing and deployment.
  8. Disaster Recovery: Docker’s container images and volume management simplify backup and restore processes, enhancing disaster recovery plans.
Benefits of using Docker

When to Use Docker

Docker is the best development solution for the following:

  • Microservices Architecture: Docker provides the necessary isolation and scalability required for developing microservices-based applications.
  • Continuous Integration and Continuous Deployment (CI/CD): Docker offers consistent environments for testing and deployment, thereby ensuring efficient CI/CD workflows.
  • Development and Testing: Docker allows developers to create consistent development and testing environments. This reduces the setup time and avoids configuration issues.
  • Cloud Migration: Docker helps in migrating applications to the cloud by packaging them into lightweight, portable containers.
  • Resource Optimization: Docker’s containers can consolidate workloads and optimize server resources, thereby improving resource utilization.
  • DevOps Practices: Docker aligns well with DevOps practices by automating infrastructure provisioning and deployment.

When Not to Use Docker

Although Docker has such a range of applications, there are certain scenarios where you can find better options. So let’s find out where we shouldn’t be using Docker.

  • Desktop Applications: Docker is best suited for server-based applications. For desktop applications that require a rich graphical user interface, Docker may not meet all requirements.
  • Simple and Small Projects: For small projects with simple architectures, Docker’s complexity and overhead might be unnecessary.
  • Multi-OS Development Pipelines: Docker uses the host system’s kernel, so running containers that require different kernels can be challenging. In such cases, virtual machines might be a better option.
  • Performance-Only Improvements: If the primary goal is to boost application performance without other DevOps benefits, alternative tools might be more suitable.
  • Easily Managed Technology: Docker requires a good understanding of containerization and related technologies. If a project needs a quick and simple technology setup, Docker might not be the best choice.

Conclusion

As you have seen in this article, Docker offers a myriad of use cases across various industries. From improving development efficiency and scalability to enhancing security and compliance, Docker provides you with a platform for all your development needs. It has helped various global organizations modernize their IT infrastructure, streamline operations, and drive innovation in today’s competitive landscape.

Whether you’re looking to optimize resource usage, accelerate software delivery, or innovate with new technologies, Docker’s versatility makes it an indispensable tool for modern software development and deployment. So go ahead and implement it in your projects to make your life easier!

Frequently Asked Questions

Q1. What is Docker?

A. Docker is a powerful platform-as-a-service (PaaS) that enables developers to build, deploy, and run applications in containers. These containers package an application along with its dependencies and configurations into a single executable unit. This way, it ensures the application’s consistency across various computing environments.

Q2. What are the main uses of Docker?

A. The main use cases of Docker include improving development efficiency, facilitating scalability, enhancing security, and adhering to compliance standards.

Q3. When should I use a Docker?

A. Docker works best for projects that involve microservices architecture, continuous integration & deployment, consistent development and testing, cloud migration, resource optimization, or DevOps practices.

ApplicationsClouddeploymentDockerDocker ContainersGuidelisticlesoftware developmentuses
K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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