DevOps with Multi Cloud
The DevOps with Multi-Cloud Training program provides comprehensive, hands-on training in modern DevOps practices across leading cloud platforms, including AWS, Microsoft Azure, and Google Cloud Platform (GCP).
The course covers cloud deployment, infrastructure management, containerization, CI/CD automation, infrastructure as code, cloud automation, monitoring, and workload migration. It is designed to help learners develop practical skills for implementing and managing DevOps workflows across multi-cloud environments.
Who Can Join This Course?
This course is suitable for:
- DevOps Aspirants
- Cloud Engineers
- System Administrators
- Linux Administrators
- Software Developers
- IT Professionals
- Cloud Engineers
- DevOps Engineers
- Fresh Graduates
- Working Professionals looking to transition into DevOps and Cloud careers
This course provides in-depth knowledge of DevOps methodologies and multi-cloud technologies. Learners will gain practical experience working with AWS, Azure, and GCP while learning how to deploy, automate, monitor, and manage applications across different cloud platforms.
You will work with industry-relevant tools such as Git, Jenkins, Docker, Kubernetes, Helm, Ansible, Terraform, Argo CD, Prometheus, Grafana, SonarQube, and Python Boto3.
The training emphasizes practical implementation, automation, CI/CD pipelines, cloud infrastructure management, container orchestration, monitoring, and real-world DevOps workflows.
Why Choose Our DevOps with Multi-Cloud Training?
- Industry-oriented curriculum
- Hands-on practical training
- Real-time project exposure
- Training on AWS, Azure & GCP
- Practical experience with industry-standard DevOps tools
- CI/CD pipeline implementation
- Docker & Kubernetes training
- Infrastructure automation with Terraform & Ansible
- Cloud automation using Python Boto3
- Monitoring and logging implementation
- Career-focused DevOps training
- Modern DevOps with Multi-Cloud
Learn and implement modern DevOps practices across multiple cloud environments. - AWS, Azure & GCP
Gain practical knowledge of the three major cloud platforms and their core services. - Cloud Deployment & Operations
Learn to deploy, configure, manage, and optimize cloud-based applications and infrastructure. - Docker & Kubernetes
Master application containerization and orchestration using Docker and Kubernetes. - CI/CD Automation
Build automated CI/CD pipelines using Jenkins, Git, GitLab CI/CD, and GitOps practices. - Infrastructure as Code
Automate infrastructure provisioning and management using Terraform. - Configuration Management
Learn infrastructure and application configuration using Ansible. - Cloud Automation with Python
Automate AWS cloud operations and services using Python and Boto3. - Monitoring & Logging
Monitor applications and infrastructure using Grafana, Prometheus, and EFK Stack. - Workload Migration & Optimization
Understand multi-cloud migration strategies and techniques for improving cloud performance.
By the end of this course, learners will be able to:
- Understand and implement modern DevOps practices in multi-cloud environments.
- Work with AWS, Azure, and GCP cloud platforms.
- Deploy and manage applications across multiple cloud environments.
- Configure and manage cloud infrastructure and networking.
- Build automated CI/CD pipelines using Jenkins and Git-based tools.
- Containerize applications using Docker.
- Deploy and manage containers using Kubernetes and Helm.
- Automate infrastructure using Terraform and Ansible.
- Automate AWS services and operations using Python and Boto3.
- Implement continuous monitoring using Prometheus and Grafana.
- Configure centralized logging using EFK Stack.
- Understand GitOps and application deployment using Argo CD.
- Plan and implement workload migration and cloud optimization.
Learners are recommended to have:
Basic knowledge of DevOps concepts and cloud computing.
Basic understanding of at least one cloud platform such as AWS, Azure, or GCP.
Familiarity with CI/CD concepts.
- Basic understanding of Docker and containerization.
- Basic knowledge of Linux and command-line operations.
- Basic programming or scripting knowledge, preferably Python.
Job Roles / Industry Positions
- DevOps Engineer
- Cloud DevOps Engineer
- Multi-Cloud Engineer
- Cloud Engineer
- Site Reliability Engineer (SRE)
- Infrastructure Automation Engineer
- Kubernetes Engineer
- CI/CD Engineer
- DevOps Automation Engineer
- Cloud Automation Engineer
- Release Engineer
- DevSecOps Engineer
- Platform Engineer
- Infrastructure Engineer
- Cloud Operations Engineer
- Cloud Infrastructure Engineer
- Build & Release Engineer
- Automation Engineer
- Systems Engineer
- Cloud Architect
- Cloud Computing Fundamentals
- AWS, Azure & GCP Cloud Overview
- Introduction to Cloud Computing
- AWS EC2, Azure Virtual Machines & GCP Compute Engine
- Cloud Networking Fundamentals
- AWS VPC, Azure VNet & GCP Networking
- AWS, Azure & GCP Storage Services
- Identity & Access Management (IAM)
- Cloud Security Fundamentals
- Linux & Shell Scripting
- Linux Introduction
- Linux File System
- Linux Commands
- User & Permission Management
- Process Management
- Package Management
- Shell Scripting
- Automation Using Shell Scripts
- AWS Cloud Services
- Amazon EC2
- Amazon S3
- Amazon RDS
- Amazon DynamoDB
- AWS IAM
- AWS Security Services
- AWS Lambda
- Amazon Route 53
- AWS Networking
- DevOps Fundamentals
- Introduction to DevOps
- DevOps Lifecycle
- DevOps Culture & Practices
- Continuous Integration
- Continuous Delivery
- Continuous Deployment
- DevOps Tools & Workflow
- Version Control with Git
- Introduction to Git
- Git Installation & Configuration
- Git Repository Management
- Branching & Merging
- Git Workflows
- Remote Repositories
- GitHub/GitLab Integration
- Build Automation & Code Quality
- Maven Introduction
- Maven Project Structure
- Build Lifecycle
- Dependency Management
- SonarQube Overview
- Code Quality & Static Code Analysis
- Integrating SonarQube with CI/CD
- Jenkins & CI/CD
- Introduction to Jenkins
- Jenkins Installation & Configuration
- Jenkins Jobs
- Pipeline Concepts
- Jenkins Pipeline
- Automated Build & Deployment
- Jenkins with Git
- CI/CD Pipeline Implementation
- Docker
- Introduction to Containers
- Docker Architecture
- Docker Installation
- Docker Images
- Docker Containers
- Dockerfile
- Docker Volumes
- Docker Networking
- Docker Compose
- Docker with CI/CD
- Kubernetes
- Introduction to Kubernetes
- Kubernetes Architecture
- Pods
- Deployments
- Services
- ConfigMaps & Secrets
- Namespaces
- Persistent Volumes
- Kubernetes Networking
- Application Deployment
- Kubernetes with Cloud Platforms
- Helm
- Introduction to Helm
- Helm Architecture
- Helm Charts
- Chart Configuration
- Application Deployment Using Helm
- Managing Kubernetes Applications with Helm
- EFK Stack & Logging
- Introduction to Centralized Logging
- Elasticsearch
- Fluentd/Fluent Bit
- Kibana
- Log Collection & Processing
- Centralized Application Logging
Monitoring and Troubleshooting
- GitOps & Argo CD
- Introduction to GitOps
- GitOps Principles
- Argo CD Overview
- Argo CD Installation
- Application Deployment Using Argo CD
- Continuous Deployment with GitOps
- Monitoring with Grafana & Prometheus
- Introduction to Monitoring
- Prometheus Overview
- Prometheus Architecture
- Metrics Collection
- Grafana Overview
- Dashboard Creation
- Application & Infrastructure Monitoring
- Alerts & Notifications
- Ansible
- Introduction to Ansible
- Ansible Architecture
- Inventory Management
- Playbooks
- Variables
- Roles
- Ansible Modules
- Configuration Management
- Application Deployment Using Ansible
- Terraform
- Introduction to Infrastructure as Code
- Terraform Architecture
- Terraform Installation
- Providers
- Resources
- Variables & Outputs
- Terraform State
- Modules
- Infrastructure Provisioning
- Multi-Cloud Infrastructure with Terraform
- Python & Boto3
- Python Fundamentals for DevOps
- Introduction to Boto3
- AWS SDK for Python
- Automating AWS Services
- EC2 Automation
- S3 Automation
- IAM Automation
- Cloud Resource Management
- DevOps Automation Using Python
- Multi-Cloud Deployment & Optimization
- Multi-Cloud Architecture
- Workload Deployment Across AWS, Azure & GCP
- Multi-Cloud Management
- Cloud Migration Strategies
- Application Migration
- Performance Optimization
- Cost Optimization
- High Availability & Scalability
- Real-Time Multi-Cloud DevOps Implementation
After completing the course, learners can explore roles such as:
- DevOps Engineer
- Cloud Engineer
- Cloud DevOps Engineer
- AWS DevOps Engineer
- Azure DevOps Engineer
- Multi-Cloud Engineer
- Site Reliability Engineer (SRE)
- Infrastructure Engineer
- Automation Engineer
- Cloud Automation Engineer
- Kubernetes Administrator
