4.8 (120+ reviews)|500+ professionals trained|12+ batches
DevOps · Cloud · AI Training
Master AI-Powered DevOps with AWS & Azure
100 Days Master Certification Program — even if you’re a complete beginner.
23 Modules16+ Real ProjectsAI-Powered WorkflowsPlacement Support
Learners from TCS, Infosys, Wipro, HCL, Cognizant & more
Next batch starts soon — limited seats
00
Days00
Hours00
Minutes00
SecondsYour Outcome
What You'll Become
This program is engineered to turn you into a job-ready, AI-fluent DevOps professional.
₹6–18 LPA
Typical DevOps salary range in India
16+
Production-grade projects you ship
100 Days
From beginner to job-ready
1:1
Mentorship & placement guidance
Roles you can target after this program
DevOps EngineerCloud EngineerSite Reliability Engineer (SRE)Platform EngineerInfrastructure EngineerDevSecOps Engineer
Enrollment
Choose Your Plan
Flexible payment options to start your DevOps journey today.
Most Popular
Full Payment
₹10,000
- Details: Pay upfront and secure your spot in the course.
Installment Payment
Two installments of ₹5,000 each
- First Installment: Due at the time of registration.
- Second Installment: Due within 30 days of the course start date.
What You'll Learn in 100 Days
A
Ansible
K
Kubernetes
J
JFrog
T
TrivyO
OWASP ZAP
D
Dependency Check🤖 AI Native
AI Tools Involved
AWS Kiro
Claude
Amazon Q
WorKiK
GitHub Copilot
Azure Copilot
Google Duet AI
TGPT
Why These AI Tools?
☁️
Cloud AI Assistants
Tools:
AWS Kiro, Amazon Q, Azure Copilot, Google Duet AI
Focus:
- Generate Infrastructure as Code
- Debug cloud issues
- Cost optimization
- Kubernetes YAML generation
🛠️
Terraform Automation
Tools:
Workik
Focus:
- Generate Terraform modules
- Improve security
- Fix configuration errors
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AI Code Assistants
Tools:
GitHub Copilot, Claude
Focus:
- Code suggestions
- Dockerfile generation
- CI/CD pipeline creation
- Documentation automation
>_
Terminal Enhancement
Tools:
TGPT (Terminal GPT)
Focus:
- Linux command help
- Shell scripting
- Log analysis
- Server troubleshooting
23 Modules
Course Modules
Everything you'll master across 100 days — from Linux fundamentals to AI-powered DevOps.
- 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
- Shell history and introduction
- Types of shells
- Shebang line in shell
- Command line arguments
- Variables
- Types of Operators
- Loops
- Case Statement
- Functions
- 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
- 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
- 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)
- 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
- 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
- GitHub 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
- 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
- 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
- Setting up a JFrog Artifactory Account
- Configuring JFrog Artifactory Account
- Adding an Artifactory Stage in Jenkins
- Publishing a JAR to JFrog Artifactory from Jenkins
- 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
- 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
- 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
- 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
- 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, System Environment & Global Properties
- Create and configure a job · Run manually · Triggering a Build
- 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
- What is GitLab?
- GitLab Modules
- Understanding & executing basic Git commands
- The “git add” and “git commit” commands
- Setting Git configurations
- Reviewing repository history
- Merging a branch & using “git stash”
- Introduction to GitLab terminology
- Getting started with GitLab · Account Authentication
- GitLab Group Dashboard Interface
- Creating a new project · Packages & Registry section
- Creating a new issue in the project
- Introduction to GitLab Flow · branching strategies
- GitHub Flow · Git Flow · GitLab Flow
- Using Markdown syntax · modifying the Readme file
- git push & git pull · creating merge requests
- Understanding Production Branch
- Introduction to GitLab CI/CD · terminologies
- Defining a GitLab CI/CD Pipeline · Pipeline editor
- Pipeline variables · reviewing jobs & environments
- Walkthrough of the codebase · writing a GitLab pipeline
- Adding environment variables · artifact keyword
- Comparison of GitLab pipeline and Jenkins Pipeline
- Triggering the GitLab pipeline
- GitLab releases · Package / Container / Infrastructure Registry
- Reviewing pom.xml · writing GitLab pipeline · creating tags
- 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 the monitoring stack with 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
- Service discovery in Prometheus to scrape app metrics
- Grafana dashboards for application-specific monitoring
- Integrating Grafana with GitHub · visualizing GitHub metrics
- Introduction to Elasticsearch · Elasticsearch vs OpenSearch
- Installation & Hosting · Basic Architecture
- Sharding & Replication · Node Roles
- Index & Document Management · Routing & Versioning
- Batch Processing & Data Import · Analysis & Mapping · Analyzers
- Searching · Parent-Child Relationships · Result Formatting
- Aggregations · Advanced Search Features
- 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
- Why Python for DevOps?
- Installing Python and setting up the environment
- Writing and executing Python scripts
- Python datatypes (int, float, string, list, tuple, dict, set)
- Variable declaration and best practices
- Defining and calling functions
- 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 · creating and modifying JSON objects
- Introduction to databases and SQL
- Using Python with databases (PostgreSQL, MySQL)
- Setting up PostgreSQL · connecting Python to PostgreSQL
- Introduction to AWS SDK (boto3) · automating AWS services
- docker-py library · automating Docker container management
- Running and managing containers programmatically
- Python kubernetes-client SDK
- Automating Kubernetes deployments using Python
- Managing Kubernetes resources (pods, deployments, services)
- AWS Kiro
- Amazon Q
- Azure Copilot
- Google Duet AI
- Workik (Terraform)
- Github Copilot (code suggestion, etc.)
- Azure Copilot (Azure, Bicep)
- Google Duet AI (GCP infra and Kubernetes)
- 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
- 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
- Resume Preparation
- Frequently Asked Interview Questions
- Mock Interviews
- LinkedIn Optimization
Hands-On
Capstone Projects
Build a portfolio of production-grade projects you can showcase to employers.
Flask Web Application with CI/CD
Tech Stack:
JenkinsDockerGitHubPython
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:
DockerPythonPostgreSQLRedis
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:
KubernetesYAMLEKSHelm
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:
TerraformAWSEC2VPC
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:
PrometheusGrafanaELKKibana
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:
TerraformAWSEC2VPC
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:
PrometheusDockerTerraformFlask
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
Freshers
Application Developers
Security Engineers
Cloud Professionals
Practitioners
Software Engineers
Site Reliability Engineers
Looking for DevOps Internship
Solution Architects
Infrastructure Architects
Technical Leads
Individuals aiming to build credibility as experienced DevOps professionals
Aspirants looking to work as DevOps professionals
Bonus Features
🚀
Real-World Projects
Automated CI/CD pipeline for real-time code integration and deployment.
🔓
Lifetime Access
Materials and recordings are available even after the training ends.
🎧
Free Support
Post-training, we will provide end to end guidance for placement.
🔁
Attend Any Batch
If you have enrolled in a batch, you can attend other batch for free.
Why Attend This Training?
Expert Instruction: Learn from industry professionals who bring real-world experience.
Networking Opportunities: Connect with peers and expand your professional network.
Career-Boosting Skills: Mastering DevOps with AWS is a must-have skill for developers and tech professionals.
Supportive Community: Join a collaborative environment for sharing knowledge and resources.
Interactive & Hands-On: Experience real-world scenarios, exercises, and guidance from industry experts.
Certification: Receive a training and internship certificate of completion to boost your resume and showcase your new skills.
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100 Days
23 Modules
Certificate
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Frequently Asked Questions
Yes. On completion you receive a training and internship certificate that you can add to your resume and LinkedIn.
No prior experience is required. The program starts from Linux fundamentals and is designed for beginners as well as working professionals.
Yes. All session recordings and materials remain available to you even after the training ends.
The program runs for 100 days with daily live online/offline sessions covering 23 modules end to end.
Yes. We provide end-to-end placement guidance and post-training support, and you can re-attend any future batch for free.
100 Days Master Program
₹10,000 / full