Daily Online Live Training

MASTER DEVOPS WITH MULTI - CLOUD ( AWS & AZURE )

100 DAYS MASTER CERTIFICATION PROGRAM

even if you are a beginner& don't have any technical knowledge.

FULL PAYMENT

  • Cost:₹ 7999

  • Details: Pay upfront and secure your spot in the course.

INSTALLMENT PAYMENT

  • Cost:Two installments of ₹4,000 each

  • Details:First Installment: Due at the time of registration. Second Installment: Due within 30 days of the course start date.

What You'll Learn in this 100 Days

Linux

Shell Scripting

Git & GitHub

Apache Maven

Jenkins

Sonatype Nexus

Ansible

Docker

Kubernetes

Monitoring Tools

Terraform

AWS & Azure Cloud

Splunk

Argo CD

Python

Gitlab

Apache Tomcat

Jira

JFrog

BitBucket

Trivy

Owasp Zap

Hashicorp vault

Dependency Check

Module 1 : Linux

  • Linux Overview

  • What is Operating System

  • What is Unix, Linux

  • Linux vs Windows

  • Linux Flavors

  • Linux Architecture

  • Linux Commands

  • Reading Files

  • Redirection Operators

  • User Management

  • Group Management

  • File System Management

  • Editors

Module 2 : Shell Scripting

  • Shell history and introduction

  • Types of shells

  • Shebang line in shell

  • Command line arguments

  • Variables

  • Types of Operators

  • Loops

  • Case Statement

  • Functions

Module 3: AWS Cloud

  • Introduction to Cloud Computing

  • AWS Services

  • VM Creation in AWS

  • Elastic Compute Cloud (EC2)

  • AWS Regions and Availability Zones

  • Amazon Machine Images (AMI)

  • EC2 Instances

  • Amazon Elastic Block Store (EBS)

  • Load Balancing (ELB)

  • Auto Scaling

  • Network & Security

  • Amazon Virtual Private Cloud (VPC)

  • Amazon Route 53

  • Identity Access Management (IAM)

  • Amazon S3

  • Relational Database Service (RDS)

  • Amazon Cloud Watch

  • Cloud Formation

  • Amazon Simple Queue Service (SQS)

  • Elastic Bean Stalk

  • AWS Lambdas

Module 4: Azure Cloud

  • Introduction

  • Azure Essentials

  • Azure Virtual Machines

  • Azure ARM Template

  • Azure Tags

  • Azure Network Security Group

  • Azure Virtual Machine User Data & Custom Data

  • Azure Load Balancer

  • Azure Scale Set

  • Azure Resource Locks

  • Azure Functions

  • Azure Kubernetes Services

  • Azure Networking

  • Bastion Server/ Jump Host in Azure

  • Azure Native Bastion Service

  • VNet Peering

  • Public DNS Zone

  • Azure + Terraform

Module 5 : Terraform (Infrastructure as code - IAS)

  • Challenges with Traditional IT Infrastructure

  • Types of IAC Tools

  • Why Terraform?

  • Installing Terraform

  • HashiCorp Configuration Language (HCL) Basics

  • Update and Destroy Infrastructure

  • Using Terraform Providers

  • Configuration Directory

  • Multiple Providers

  • Using Input Variables

  • Understanding the Variable Block

  • Using Variables in Terraform

  • Resource Attributes

  • Resource Dependencies

  • Output Variables

  • Introduction to Terraform State

  • Purpose of State

  • Terraform State Considerations

  • Terraform Commands

  • Mutable vs Immutable Infrastructure

  • LifeCycle Rules

  • Datasources

  • Meta-Arguments

  • Count

  • for-each

  • Version Constraints

  • Getting Started with AWS

  • Demo Setup an AWS Account

  • Introduction to IAM

  • Demo IAM

  • Programmatic Access

  • AWS IAM with Terraform

  • IAM Policies with Terraform

  • Introduction to AWS S3

  • S3 with Terraform

  • Introduction to DynamoDB

  • Demo Dynamodb

  • DynamoDB with Terraform

  • What is Remote State and State Locking?

  • Remote Backends with S3

  • Terraform State Commands

  • Introduction to AWS EC2

  • Demo: Deploying an EC2 Instance

  • AWS EC2 with Terraform

  • Terraform Provisioners

  • Provisioner Behaviour

  • Considerations with Provisioners

  • Terraform Taint

  • Debugging

  • Terraform Import

  • What are Modules?

  • Creating and Using a Module

  • Using Modules from the Registry

  • More Terraform Functions

  • Conditional Expressions

  • Terraform Workspaces (OSS)

Module 6 : DevOps and SDLC Lifecycle

  • DevOps overview

  • Key stakeholders of DevOps

  • What is SDLC?

  • Phases of SDLC

  • Role of Developers in SDLC

  • Role of Operations in SDLC

  • Waterfall Model

  • Advantages of Waterfall

  • Disadvantages of Waterfall

  • Agile Development Process

  • Agile Manifesto

  • Agile Scrum Workflow

  • Agile Analysis Estimation Techniques

  • Types of Roles and Responsibilities in Agile

  • Problems That DevOps Solves

  • DevOps Lifecycle Overview

  • Core DevOps Tools

  • DevOps Technology Categories

  • Collaboration Tools

  • Planning Tools

  • Configuration Management Tools

  • Source Control

  • Development Environments

  • Continuous Integration

  • Continuous Testing

  • Continuous Deployment

Module 7 : Git & GitHub & Bitbucket (Source Code Management)

  • Introduction

  • What is a Version Control System (VCS)?

  • Distributed Vs Non-Distributed

  • VCS

  • What is Git and where did it come from?

  • Alternatives to Git

  • Git Hub Account Setup

  • Obtaining Git Installing Git

  • Key Terminology

  • Staging Vs Un-Staging

  • Adding Files to Staging Areas

  • Removing Files from Staging Area

  • Commit to Local Repository

  • Pull Request

  • Push to Central Repository

  • Repository Cloning

  • Stashes & Stash Apply

  • Branching in Git

  • Why We need Branches

  • Cloning & Switching Branches

  • Fetching Changes (git fetch)

  • Rebasing (git rebase)

  • Git Pull

  • Git Conflicts

  • Branch Merging

  • Merging & Re Basing

  • Deleting a Branch

Module 8 : Ansible (Configuration Management)

  • What is Configuration Management

  • What is Ansible

  • Installing Ansible

  • Testing with First Ansible Commands

  • Introduction to Play Books

  • YML File

  • Writing Play Books

  • Play Books Execution

  • Tags

  • Handlers

  • Introduction to Roles

  • Role Basics

  • Creating Role

  • Ansible Galaxy

  • Ansible Tower

Module 9 : Maven (Build Tools)

  • What is Build Tool

  • Automated build process

  • Maven Introduction & Objectives

  • Maven Installation

  • Maven Terminology

  • Maven Archetypes

  • Maven Project Creation

  • Maven Dependencies

  • Maven Repositories (Local Repo, Central Repo, Remote Repo)

  • Maven Goals

Module 10 : JFrog

  • Setting up a JFrog Artifactory Account

  • Configuring JFrog Artifactory Account

  • Adding an Artifactory Stage in Jenkins

  • Publishing a JAR to JFrog Artifactory from Jenkins

Module 11 : Tomcat

  • What is Apache Tomcat?

  • Tomcat vs. other Java application servers (WildFly, GlassFish, etc.)

  • Role in Java EE (Jakarta EE) architecture

  • Supported Java Servlets, JSP specifications, and APIs

  • Tomcat versions and compatibility with JDK

  • Prerequisites (Java JDK, environment variables)

  • Downloading and installing Tomcat

  • Directory structure overview (bin, conf, webapps, logs, lib, temp, work)

  • Starting and stopping Tomcat (Windows, Linux)

  • Using the Tomcat Manager application

  • Deploying and undeploying web applications

  • WAR file deployment

  • Hot deployment vs. cold deployment

  • User authentication for manager app (tomcat-users.xml)

  • server.xml – connectors, ports, services, and hosts

  • web.xml – servlet and filter configuration

  • context.xml – application-specific settings

Module 12 : Docker ( Containerization Tool)

  • What is Docker

  • Life without Docker

  • Life with Docker

  • Installing Docker on Linux

  • What is container

  • Docker run command

  • Working with images

  • Container Life cycle

  • Docker File

  • Docker Network

  • Docker Volumes

  • Docker Compose

  • Docker Swarm

  • Spring Boot App with Docker

  • Python App with Docker

  • MYSQL with Docker

Module 13 : Kubernetes

  • What is Kubernetes

  • Docker Swarm Vs Kubernetes

  • Kubernetes Architecture

  • Control Plane

  • Worker Nodes

  • Namespaces

  • Pods

  • Pod Life cycle

  • Services (Cluster IP, Node Port, Load Balancer)

  • Replication Controller

  • Replication Set

  • Daemon Set

  • Stateful Set

  • Deployment (Recreate, Rolling Update, Blue Green)

  • Config Map

  • Secrets

  • Ingress Controller

  • HELM Charts

Module 14 : Sonatype Nexus Repository

  • Introduction to Sonatype

  • What is Artifact Repo

  • Nexus Introduction

  • Nexus Setup

  • Snap Short Repository

  • Release Repository

  • Shared Libs

  • Maven with Nexus Repo Integration

  • Uploading Build Artifacts

Module 15 : Jenkins (Continuous Integration Tool)

  • Introduction to Jenkins

  • How to achieve Continuous Integration with Jenkins

  • Jenkins Server Setup

  • Jenkins Jobs

  • How to integrate Jenkins with Maven

  • Jenkins dashboard

  • Jenkins plugins – how to download and use

  • Setup and Running Jenkins Jobs

  • Configure Dashboard Configure System Environment Global Properties

  • Create and configure a job Run a job manually Triggering a Build Scheduled

  • Build job Manual Build job

  • Polling SCM

  • Post-Build Actions Archiving Build Results Notifications

  • Jenkins Plugins

  • Jenkins Master Slave Architecture

  • Jenkins Pipeline Introduction

  • Multi Stage Pipeline

  • Jenkins with Maven & Git Integration

  • Jenkins with Sonar Integration

  • Jenkins with Nexus Integration

  • Jenkins with Docker Integration

  • Jenkins with Kubernetes Integration

Module 16: GitLab

  • What is GitLab?

  • GitLab Modules

  • Understanding the basic Git commands

  • Executing Git commands

  • What is the “Git .add” command?

  • Using the “Git commit” command

  • How to set Git configurations?

  • How to review the history of the repository?

  • How to merge a branch in Git?

  • Using the “Git stash” command

  • Introduction to GitLab terminology

  • Getting started with GitLab

  • GitLab Account Authentication

  • GitLab Group Dashboard Interface

  • Creating a new project in GitLab

  • Exploring Packages & Registry section

  • Creating ‘new issue’ in the project

  • Introduction to GitLab Flow Tutorial

  • What is a branching strategy?

  • Common Git Branching Strategy

  • GitHub Flow

  • Git Flow

  • GitLab Flow

  • Getting started with applying GitLab Flow

  • Using Markdown syntax

  • Modifying the “Readme” file locally in the machine

  • Understanding the “git push & “git pull’ command

  • Creating “merge request”

  • Making changes in the “Readme” file

  • Understanding Production Branch

  • Introduction to GitLab CI/CD Tutorial

  • Important GitLab CI/CD terminologies

  • Getting started with GitLab Cl/CD Pipeline

  • Defining GitLab CI/CD Pipeline for a project

  • Getting familiar with the Pipeline editor

  • Defining pipeline variables to run the desired job

  • Reviewing the jobs

  • Reviewing the environment for Deploying the jobs

  • Introduction to GitLab CI Tutorial

  • Walkthrough of the codebase

  • Writing an equivalent GitLab pipeline

  • Adding environment variables in GitLab

  • Example of artifact keyword in GitLab

  • Comparison of GitLab pipeline and Jenkins Pipeline

  • Triggering the GitLab pipeline

  • GitLab releases overview

  • GitLab Package Registry

  • GitLab Container Registry

  • GitLab Infrastructure Registry

  • Walkthrough of the code inside the test pipeline project

  • Reviewing the “pom.xml” file

  • Writing GitLab pipeline

  • Creating tags in the pipeline

Module 17 : Monitoring Tools

  • Introduction to monitoring and observability

  • Installing and configuring Prometheus

  • Setting up Node Exporter for system metrics

  • Using Black Box Exporter for external endpoint monitoring

  • Installing and configuring Grafana

  • Connecting Prometheus as a data source in Grafana

  • Creating custom dashboards and visualizations

  • Setting up alerting rules in Prometheus

  • Configuring Alertmanager for notifications

  • Sending alerts via email, Slack, or other integrations

  • Running Prometheus, Node Exporter, Black Box Exporter, Alertmanager, and Grafana

  • using Docker

  • Managing monitoring stack with Docker Compose

  • Deploying Prometheus and Grafana in a Kubernetes cluster

  • Collecting and visualizing node-level metrics

  • Setting up alerts for CPU, memory, and disk usage

  • Monitoring application performance in Kubernetes

  • Using service discovery in Prometheus to scrape application metrics

  • Creating dashboards in Grafana for application-specific monitoring

  • Integrating Grafana with GitHub for tracking repository activities

  • Visualizing GitHub metrics using Grafana dashboards

  • Introduction to Elasticsearch

  • Elasticsearch vs OpenSearch

  • Installation & Hosting

  • Basic Architecture

  • Sharding & Replication

  • Node Roles

  • Index Management

  • Document Management

  • Routing & Versioning

  • Batch Processing & Data Import

  • Analysis & Mapping

  • Analyzers

  • Searching

  • Parent-Child Relationships

  • Result Formatting

  • Aggregations

  • Advanced Search Features

Module 18: Argo CD

  • GitOps & Argo CD Fundamentals

  • Setting Up Argo CD: Installation, User Management

  • Managing Applications & Sync Strategies

  • Continuous Delivery & Rollbacks

  • Syncing, Diffing & Monitoring

  • Application Security & Best Practices

  • Customizations, CRDs & Webhooks

  • Troubleshooting & Debugging

Module 19: Python

  • Why Python for DevOps?

  • Installing Python and setting up the environment

  • Writing and executing Python scripts

  • Understanding Python datatypes (int, float, string, list, tuple, dict, set)

  • Variable declaration and best practices

  • Defining and calling functions

  • Understanding function arguments (positional, keyword, default)

  • Creating and importing modules

  • Using built-in and third-party Python libraries

  • if-else conditions

  • for and while loops

  • Loop control statements (break, continue, pass)

  • Reading and writing files (open(), read(), write())

  • Working with CSV and JSON files

  • File handling best practices

  • Understanding socket programming

  • Creating a simple client-server connection in Python

  • Parsing JSON data using Python

  • Creating and modifying JSON objects

  • Introduction to databases and SQL

  • Using Python with databases (PostgreSQL, MySQL)

  • Setting up a PostgreSQL database

  • Connecting Python to PostgreSQL

  • Introduction to AWS SDK (boto3)

  • Automating AWS services

  • Introduction to the docker-py library

  • Automating Docker container management using Python

  • Running and managing containers programmatically

  • Using Python kubernetes-client SDK

  • Automating Kubernetes deployments using Python

  • Managing Kubernetes resources (pods, deployments, services)

Module 20: Security Tools (DevSecOps)

  • Security Tools Overview

  • Trivy Setup & File System Scanning

  • OWASP Dependency Check Setup & Usage

  • Tools like Prowler, Dockle, OWASP ZAP

  • Security Tools Integrations With CI/CD

  • SBOM (Software Bill of Materials) Hands-On Usage

  • HashiCorp Vault

Module 21: Real-time Projects

  • Java Application Deployment

  • Python Application Deployment

  • Angular Application Deployment

  • React JS Application Deployment

  • Fullstack Application Deployment (SpringBoot + Angular)

  • AWS Architecture Creation with Terraform

  • ArgoCD with GitOps Deployments

  • Docker Advanced Project

  • Ansible Automation and Roles Creation

  • Flask Web Application with CI/CD

  • Containerized Microservices Architecture

  • Kubernetes Cluster Management

  • Infrastructure as Code with Terraform

  • Monitoring and Alerting System

  • Complete DevOps Pipeline Integration

  • ML Model Deployment Pipeline

Module 22: Interview Guide

  • Resume Preparation

  • Frequently Asked Interview Questions

  • Mock Interviews

  • LinkedIn Optimization

Capstone Projects

Flask Web Application with CI/CD

Tech Stack Used Here:

Objective:

Build and deploy a Python Flask application with automated testing and deployment pipeline

Implementation:

  • Create a Flask web application with multiple routes

  • Write unit tests using pytest framework

  • Set up Docker containerization

  • Configure Jenkins CI/CD pipeline

  • Deploy to AWS EC2 with automated rollback.

Containerized Microservices Architecture

Tech Stack Used Here:

Objective:

Design and implement a microservices architecture using Docker containers.

Implementation:

  • Break monolithic app into microservices

  • Create Docker images for each service

  • Set up inter-service communication

  • Implement Redis for caching

  • Configure PostgreSQL database

  • Use Docker Compose for orchestration

Kubernetes Cluster Management

Tech Stack Used Here:

Objective:

Deploy and manage applications on Kubernetes with auto-scaling and load balancing.

Implementation:

  • Set up Kubernetes cluster on AWS EKS

  • Create deployment and service manifests

  • Configure horizontal pod autoscaling

  • Implement load balancing with ingress

  • Set up persistent volumes

  • Monitor cluster health and performance

Infrastructure as Code with Terraform

Tech Stack Used Here:

Objective:

Provision and manage cloud infrastructure using Terraform

automation.

Implementation:

  • Design cloud architecture blueprint

  • Write Terraform configuration files

  • Create VPC, subnets, and security groups

  • Provision EC2 instances and load balancers

  • Set up S3 buckets for state management

  • Implement infrastructure versioning

Monitoring and Alerting System

Tech Stack Used Here:

Objective:

Implement comprehensive monitoring, logging, and alerting for production systems.

Implementation:

  • Set up Prometheus for metrics collection

  • Configure Grafana dashboards

  • Implement ELK stack for log aggregation

  • Create custom metrics and alerts

  • Set up notification channels

  • Build SLA monitoring system

Complete DevOps Pipeline Integration

Tech Stack Used Here:

Objective:

Integrate all previous projects into a unified, production-ready

DevOps pipeline.

Implementation:

  • Implement GitOps workflow with ArgoCD

  • Add security scanning to pipeline

  • Set up multi-environment deployments

  • Configure backup and disaster recovery

  • Implement blue-green deployment strategy

  • Create comprehensive documentation

ML Model Deployment Pipeline

Tech Stack Used Here:

Objective:

Deploy a machine learning model with complete MLOps pipeline including monitoring and A/B testing.

Implementation:

  • Prepare and validate ML model (.pkl file)

  • Create Flask API for model serving

  • Build Streamlit interface for testing

  • Containerize application with Docker

  • Set up Jenkins pipeline for ML deployments

  • Deploy to AWS EKS with auto-scaling

  • Implement model monitoring with Prometheus

  • Create infrastructure with Terraform

  • Set up A/B testing framework

  • Monitor model drift and performance

Who Should Attend?

System Administrators

Application Developers

Cloud Professionals

Software Engineers

Looking for DevOps Internship

Infrastructure Architects

Individuals aiming to build credibility and value as experienced DevOps professionals.

Freshers

Security Engineers

Practitioners

Site Reliability Engineers

Solution architects

Technical Leads

Aspirants looking to work as DevOps professionals

Bonus Features

Real-World Projects

Automated CI/CD pipeline for real-time code integration and deployment.

Lifetime

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the training ends.

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Why Attend This Training?

  • Expert Instruction: Learn from industry professionals who bring real-world experience.

  • Career-Boosting Skills: Mastering devops with aws is a must-have skill for developers and tech professionals.

  • Interactive & Hands-On: Experience real-world scenarios, exercises, and guidance from industry experts.

  • Networking Opportunities: Connect with peers and expand your professional network.

  • Supportive Community: Join a collaborative environment for sharing knowledge and resources.

  • Certification: Receive a training and internship certificate of completion to boost your resume and showcase your new skills.

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Frequently Asked Questions

Will I get a certificate?

Yes! A certificate is awarded to those who successfully complete the training.

Do I need any prior experience?

No, this training is designed for beginners to advanced learners.

Will I have lifetime access to the training materials?

Yes, you will have lifetime access to the training materials.

What is the duration of the training?

The training program lasts 100 days.

Is support provided after training?

Yes. For those who enrolled in this training will be added to a community where technical support team members will answer your queries.