This Guidance demonstrates how to securely run Model Context Protocol (MCP) servers on the AWS Cloud using containerized architecture. It helps organizations implement industry-standard OAuth 2.0 authentication while protecting server deployments with multiple security layers, including content delivery networks and web application firewalls.

Purely is a cloud-first microservices web application, containerized and deployed to AWS Elastic Kubernetes Service (EKS) using Helm and automated via GitHub Actions CI/CD pipelines.

A Devops pipeline is set of automated processes and tools that the development (Dev) and operations (Ops) teams implement to build, test, and deploy software faster and easier. In this course you will complete DevOps pipeline generally consists of a set of tools which are normally broken down into the following categories: Plan Code Integrate Test Release Deploy Operate This learning path will cover: Git is an open-source and distributed version control system. Github is git repository hosting service used for code sharing, bug tracking, feature request and much more. PyCharm is an integrated development environment (IDE) for python programing language. Flask is a python web framework. HTML is the standard markup language for Web pages. CSS is a style sheet language use to style a HTML document. SQLAlchemy is an open-source SQL toolkit and object-relational mapper which gives full power and flexibility of SQL. Selenium is used to automate web browser interaction. Pytest is unit testing framework that allows users to write test codes. Ngrok allows to expose a web server running on your local machine to the internet. Github Action enables you to include Continues Integration (CI) and continuous deployment (CD) capabilities and many other features directly in your repository. Docker is an open source containerization platform enables developers to package applications into containers. Docker Hub is a cloud-based repository for finding and sharing container images with your team. Kubernetes is an open-source container orchestration for automating deployment, scaling, and management of containerized applications. This course is one stop shop where you will learn web development, continuous integration, continuous deployment, containerization, writing neat and quality code, devops concepts and much more with python programing language. What you’ll learn Learn to build Continuous Integration Continuous Deployment pipeline Build CI CD tool to update docker image after any update Learn to create dockerfile Learn the fundamental concepts of Docker Learn the fundamental concepts of Kubernetes Learn to create Kubernetes YAML files Learn to deploy high availability, fault tolerance, scalable application Learn all the basic and advanced git commands Learn different types of branches like master, developer, feature, release and hotfix branch Learn fundamental concepts of Version Control System Learn to use Github actions for CI CD pipeline Learn to build python flask web application Learn to use SQL Alchemy Lean to create HTML pages using HTML, CSS and bootstrap Are there any course requirements or prerequisites? Git installed Docker installed Kubernetes installed Any IDE Github account Docker hub account Who this course is for: Anyone who wants to Enhance their skills in DevOps domain Developers and IT Pros Instructor User photo Pranjal Srivastava Docker | Kubernetes | AWS | Azure | ML | Linux | Python I am an Instructor, Devops engineer, machine learning enthusiast, cloud expert and passionate developer. I have authored 60+ courses with over 50,000+ students worldwide across 175+ countries on wide array of technologies like containerization, machine learning, Linux, programming languages and cloud computing platforms like Microsoft Azure, Amazon Web Service and IBM Cloud.

EasyECS is an innovative container deployment tool designed to streamline the process of launching containers within the Amazon Web Services (AWS) Elastic Container Service (ECS) Fargate. With its user-friendly interface and automated workflows, EasyECS empowers developers to efficiently manage containerized applications on the AWS cloud platform.

This guidance shows how to deploy a federated Kubernetes environment in Amazon Web Services (AWS) cloud using Amazon Elastic Kubernetes Service (Amazon EKS) and the open source CNCF Karmada project - multi-cluster Kubernetes management system with advanced scheduling capabilities that enable running containerized applications across clusters

This project covers the end to end implementation of deploying and productionizing a dockerized/containerized machine learning python flask application into Kubernetes Cluster using the AWS Elastic Kubernetes Service (EKS), AWS Serverless Fargate Instances, AWS CloudFormation Cloud Stack and AWS Elastic Container Registry (ECR) Service. The machine learning business case implemented in this project includes a bank note authentication binary classifier model using Random Forest Classifier; which predicts and classifies a bank note either as a Fake Bank Note (Label 0) or a Genuine Bank Note (Label 1). Implementation Steps: 1. Creation of an end to end machine learning solution covering all the ML life-cycle steps of Data Exploration, Feature Selection, Model Training, Model Validation and Model Testing on the unseen production data. 2. Saved the finalised model as a pickle file. 3. Creation of a Python Flask based API; in order to render the ML model solution and inferences to the end-users. 4. Verified and tested the working status of the Python Flask API in the localhost set-up. 5. Creation of a Docker File (containing the steps/instructions to create a docker image) for the Python Flask based Bank Note Authentication Machine Learning Application embedded with Random Forest ML Classifier Model. 6. Creation of IAM Service Roles with appropriate policies to access the AWS Elastic Container Registry (ECR) Service and AWS Elastic Kubernetes Service (EKS) and AWS CloudFormation Service. 7. Created a new EC2 Linux Server Instance in AWS and copied the web application project’s directories and its files into the AWS Linux Server using SFTP linux commands. 8. Installed the Docker software and the supporting python libraries in the AWS EC2 Linux Server Instance; as per the “requirements.txt” file. 9. Transformation of the Docker File into a Docker Image and Docker Container representing the application; using docker build and run commands. 10. Creation of a Docker Repository within the AWS ECR Service and pushed the application docker image into the repository using AWS Command Line Interface (CLI) commands. 11. Creation of the Cloud Stack with private and public subnets using the AWS CloudFormation Service with appropriate IAM roles and policies. 12. Creation of the Kubernetes Cluster using the AWS EKS Service with appropriate IAM roles and policies and linked the cloud stack created using the AWS CloudFormation Service. 13. Creation of the AWS Serverless Fargate Profile and Fargate instances/nodes. 14. Creation and configured the “Deployment.yaml” and “Service.yaml” files using the Kubernetes kubectl commands. 15. Applied the “Deployment.yaml” with pods replica configuration to the AWS EKS Cluster Fargate Nodes; using the Kubernetes kubectl commands. 16. Applied the “Service.yaml” using the Kubernetes kubectl commands; to render and service the machine learning application to the end-users for public access with the creation of the production end-point. 17. Verified and tested the inferences of the productionized ML Application using the AWS Fargate end-point created in the AWS Kubernetes EKS Cluster. Tools & Technologies: Python, Flask, AWS, AWS EC2, Linux Server, Linux Commands, Command Line Interface (CLI), Docker, Docker Commands, AWS ECR, AWS IAM, AWS CloudFormation, AWS EKS, Kubernetes, Kubernetes kubectl Commands.

This project covers the end to end implementation of deploying and productionizing a dockerized/containerized machine learning python flask application into AWS Elastic Compute Cloud (EC2) Instance and AWS Elastic Container Registry (ECR) Service. The machine learning business case implemented in this project includes a bank note authentication binary classifier model using Random Forest Classifier; which predicts and classifies a bank note either as a Fake Bank Note (Label 0) or a Genuine Bank Note (Label 1). The implementation includes below steps: 1. Creation of an end to end machine learning solution covering all the ML life-cycle steps of Data Exploration, Feature Selection, Model Training, Model Validation and Model Testing on the unseen production data. 2. Saved the finalised model as a pickle file. 3. Creation of a Python Flask based API; in order to render the ML model solution and inferences to the end-users. 4. Verified and tested the working status of the Python Flask API in the localhost set-up. 5. Creation of a Docker File (containing the steps/instructions to create a docker image) for the Python Flask based Bank Note Authentication Machine Learning Application embedded with Random Forest ML Classifier Model. 6. Creation of IAM Service Roles with appropriate policies to access the AWS Elastic Container Registry (ECR) Service and AWS Elastic Compute Cloud (EC2) Service. 7. Created a new EC2 Linux Server Instance in AWS and copied the web application project’s directories and its files into the AWS Linux Server using SFTP linux commands. 8. Installed the Docker software and the supporting python libraries in the AWS EC2 Linux Server Instance; as per the “requirements.txt” file. 9. Transformation of the Docker File into a Docker Image and Docker Container representing the application; using docker build and run commands. 10. Creation of a Docker Repository within the AWS ECR Service and pushed the application docker image into the repository using AWS Command Line Interface (CLI) commands. 11. Deployment of the dockerized/containerized Python Flask ML application into the AWS EC2 Linux Instance; with the creation of the production end-point. 12. Verified and tested the inferences of the productionized ML Application using the AWS EC2 end-point. Tools & Technologies: Python, Flask, AWS, AWS EC2, Linux Server, Linux Commands, Command Line Interface (CLI), Docker, Docker Commands, AWS ECR, AWS IAM

Code accompanying blog post for porting a containerized web application to AWS Lambda

This project deploys a highly available containerized web application within a single AWS Region. The application is securely hosted in a custom Virtual Private Cloud (VPC) that spans multiple Availability Zones, ensuring high availability and automatic scaling based on traffic demand.

Containerized web application deployed on AWS ECS Fargate with Application Load Balancer

deploy & scale your containerized web applications on AWS

This project demonstrates the design and implementation of a robust, automated CI/CD pipeline on AWS. The solution provisions a highly available and scalable infrastructure using Terraform and deploys a containerized web application using a stack of AWS Developer Tools: CodePipeline, CodeBuild, and CodeDeploy.

A fully containerized full-stack web application with automated CI/CD pipelines. Built with Docker, Terraform, and GitHub Actions,, it ensures seamless deployment to AWS using EC2, ECR, and S3. Features secure infrastructure, automated deployments, and scalability

Created a robust CI/CD pipeline for deploying a containerized web application on AWS. Leverages Terraform for infrastructure automation and ECS for container orchestration.

This project showcases the development, containerization, orchestration using Kubernetes, and deployment of a Vue.js shopping web application on AWS EKS

An interesting web scraper for collecting ASOS data. The scraper is based on Selenium, and the classes adopts the Mixin design pattern. The application is containerized using Docker and deployed on AWS EC2, the scraped data is stored in AWS S3 and AWS RDS. Using Prometheus and Grafana to monitor running indicators.

A full-stack application ( Basic ) demonstrating professional AWS deployment strategies using Node.js, Nginx, and cloud infrastructure. The project provides a comprehensive guide for deploying web applications on AWS EC2, with support for containerization, process management . Key technologies: AWS, React.js, Socket.io, Node.js, Nginx, PM2, Docker.

This repository demonstrates a scalable, secure, and cost-effective cloud architecture for a modern web application using AWS services and DevOps practices. The project features a highly available architecture with Docker containerization, managed databases, Infrastructure as Code (IaC) with Terraform, and comprehensive monitoring and logging.

Issue Tracker – A fully automated 3-tier web application (React + Node.js + MongoDB) deployed on AWS EKS with GitHub Actions CI/CD and Amazon ECR. This project demonstrates enterprise-grade DevOps practices including containerization, Kubernetes manifests, rolling updates, IAM OIDC roles, and end-to-end cloud deployment automation.

Deploy a containerized Web application in AWS EC2 using Ansible & Docker

Containerized web application showcasing Docker, Nginx, and cloud deployment on AWS EC2.

A containerized web application for list AWS resources across multiple accounts and regions.

Deploy a containerized web application on AWS EKS and expose it using ALB Ingress with Kubernetes for scalable, production-ready architecture.

A production-ready Flask web application containerized with Docker and deployed on an AWS EC2 instance behind an Nginx reverse proxy.

Cloud deployment and DevOps implementation using Docker, GitHub, and AWS EC2 for containerized static web applications with seamless cross-platform testing.

Dockerized web application deployed on AWS EC2 using Nginx container, demonstrating cloud provisioning, containerization, and public web hosting.

This open source project utilizes Kubernetes to deploy a containerized job search web application and tracking tool with Amazon EKS and AWS CloudFormation.

Developed and deployed a containerized web application on AWS using ECS Fargate with Application Load Balancer. Designed a scalable architecture by creating ECS clusters, task definitions, services, and IAM roles.

To showcase the end-to-end process of containerizing a simple web application using AWS serverless stack and ensure reliable application deployment

This is a lightweight web application that tracks and displays AWS account costs in real time using the AWS Cost Explorer API. The backend is built with Flask, containerized using Docker, and deployed securely via Render.