Multi-Cloud (AWS + Azure) DevOps

₹45,000/-

Multi-Cloud (AWS + Azure) DevOps Course near Bangalore, Hyderabad

Multi-Cloud (AWS + Azure) DevOps

Become a job-ready multi-cloud DevOps engineer with hands-on training across AWS and Azure. This program covers Linux, scripting, networking, IaC with Terraform, Kubernetes, CI/CD, DevSecOps, and GenAI/LLMOps integration through a real enterprise-style capstone project.

Course Fee: ₹45,000/-

4 MonthsDuration

Online / OfflineMode

4.8 ★Rating

3 Aug 2026Next Batch

Industry Ready

About

What this program is about and what you'll achieve.

"IntellentX's Multi-Cloud (AWS + Azure) DevOps Course in Hyderabad takes you from Linux and scripting fundamentals to enterprise-grade multi-cloud DevOps engineering."

Available both online and offline at our Hyderabad centre, this 6-month program covers networking, compute, storage, databases, identity and security, Git workflows, Infrastructure as Code with Terraform, Kubernetes orchestration, configuration management, CI/CD, and observability — mapped side by side across AWS and Azure. Every phase weaves in GenAI and LLMOps integration, culminating in a Production Capstone Project where you build and deploy an Enterprise Multi-Cloud RAG Platform, plus a dedicated project phase with real-world MLOps and DevSecOps builds. Graduates of this program go on to roles such as DevOps Engineer, Cloud Engineer, SRE, and Platform Engineer.

Curriculum aligned with NASSCOM FutureSkills Prime — India's national framework for tech skill development.

Tools & Stack

Technologies and platforms you'll work with hands-on.

AWS and Microsoft Azure (compute, networking, storage, identity, security)

Terraform, AWS CloudFormation, and Azure Bicep/ARM

Docker, Kubernetes (EKS & AKS)

Jenkins, Azure DevOps, GitHub Actions

Ansible, Puppet, HashiCorp Packer and Vault

Prometheus, Grafana, CloudWatch, and Azure Monitor

GenAI/LLMOps tooling: Bedrock, Azure OpenAI, vLLM, Ollama, LangSmith/Phoenix

Projects

Real-world projects you'll build to strengthen your portfolio.

01PROJECT 01

Production Capstone: Enterprise Multi-Cloud RAG Platform

Provisioning AWS VPC + Azure VNet architecture via Terraform & GitHub Actions. Setting up an EKS/AKS cluster with GPU nodes. Containerizing a Python FastAPI + LangChain backend querying AWS Bedrock / Azure OpenAI with pgvector. Routing traffic via Azure Front Door / CloudFront behind an ALB / AGW with SSL termination, secured with Vault / Key Vault and monitored with Prometheus + Grafana.

02PROJECT 02

Multi-Cloud End-to-End MLOps Pipeline with MLflow & EKS/AKS

Automated pipeline that trains, versions, containerizes, and deploys an ML model onto Kubernetes with zero downtime.

03PROJECT 03

Self-Healing Kubernetes Cluster with AI Log Diagnostics & Remediation

Autonomous Kubernetes monitoring system where AI diagnoses pod failure root causes and executes remediation playbooks.

04PROJECT 04

AI-Powered Cloud FinOps & Anomaly Detection Engine

Detects unexpected cloud spending spikes in real time and generates automated cost-optimization actions using AI.

05PROJECT 05

Automated GitOps PR Auditor & DevSecOps Guardrail

AI security engineer embedded into CI/CD pipelines to audit Terraform, Dockerfiles, and Kubernetes manifests before deployment.

Career Outcomes

Roles and opportunities you'll be ready for after completing this program.

DevOps Engineer

Cloud Engineer (AWS/Azure)

Site Reliability Engineer (SRE)

Platform Engineer

MLOps Engineer

Curriculum

13 modules covering everything from fundamentals to advanced topics.

Linux Administration & Shell Scripting: DevOps lab infrastructure on AWS & Azure, advanced Linux administration, Bash scripting, system administration, user management, and control flow (IF, WHILE, FOR)

PowerShell Scripting: Noun-Verb usage, module installation, user/resource management scripts, and cross-platform automation

Python Infrastructure Automation: virtual environments, data structures, environment variables (dotenv), error handling, and key modules (os, sys, json, uuid, faker, requests, boto3, Azure SDK)

API Frameworks & UI Tooling: building microservices with FastAPI (Jinja2 templates, routers), integrating external endpoints, and building developer dashboards/model interfaces with Streamlit

Cloud Architecture Foundations: AWS Global Infrastructure vs. Azure hierarchy, on-premises vs. cloud architecture, and the Shared Responsibility Model

Virtual Private Networks: AWS VPC & Azure VNet — subnets, Internet/NAT Gateways, User Defined Routes, Security Groups vs. NACLs vs. Azure NSGs/ASGs

Advanced Networking & Peering: VPC Peering, Azure VNet Peering, Hub-and-Spoke topology, Transit Gateway, and Azure Virtual WAN

Private Endpoints & Security: VPC Endpoints, Azure Private Link, AWS Network Firewall, and Azure Firewall (SNAT/DNAT, Rule Collections)

Hybrid Cloud & Edge: Virtual Private Gateway, Site-to-Site IPSec VPNs, Route 53 vs. Azure DNS, CloudFront vs. Azure Front Door, and Global Accelerator

GenAI Isolation: provisioning secure, isolated virtual network topologies to host vector search nodes and private AI endpoints

Compute Operations: EC2 vs. Azure VMs, Instance Metadata (IMDSv2), Launch Templates, AMIs, Managed Images, and Spot Instances

Storage Engineering: Block storage (EBS vs. Managed Disks), Object storage (S3 vs. Blob Storage), and Shared file systems (EFS/FSx vs. Azure Files)

Disaster Recovery & Migration: AWS Backup vs. Azure Backup/Site Recovery, SMS/DMS vs. Azure Migrate/DMS, VMware migration, and DataSync/Data Box

GenAI Storage Infrastructure: hosting uncompressed LLM model weights, training datasets, and multi-tenant document indexes on S3 and Blob storage

Load Balancing Architecture: AWS ALB/NLB vs. Azure Load Balancer/Application Gateway — target groups, health probes, and SSL/TLS termination with ACM

Web Application Firewalls: securing applications with AWS WAF and Azure WAF on Application Gateway/Front Door

Auto Scaling: EC2 Auto Scaling Groups vs. Azure VM Scale Sets, custom image baking with HashiCorp Packer, and stress testing

Global Traffic Management: AWS Route 53 Routing Policies vs. Azure Traffic Manager and Front Door

GenAI Scaling: auto-scaling model inference GPU clusters based on request volume and VRAM consumption

Relational Databases: SQL vs. NoSQL, AWS RDS MySQL/PostgreSQL & Azure SQL, Multi-AZ replication, failover testing, and Read Replicas

NoSQL & Caching: DynamoDB vs. Cosmos DB, and AWS ElastiCache vs. Azure Cache for Redis

Data Warehousing: AWS Redshift clusters vs. Azure Synapse Analytics

Serverless Computing: AWS Lambda vs. Azure Functions/Logic Apps, with API Gateway/Azure API Management

GenAI & Vector Databases: integrating pgvector on RDS/Azure PostgreSQL and indexing vector embeddings for Retrieval-Augmented Generation (RAG)

Identity & Access Management: AWS IAM vs. Microsoft Entra ID/Azure AD — Users, Groups, Roles, and Conditional Access

Directory Services & SSO: AWS Managed AD, Azure AD Connect, Single Sign-On, AWS Organizations, and Azure Management Groups

Secrets & Key Management: AWS KMS/Secrets Manager/SSM Parameter Store vs. Azure Key Vault, using Managed Identities

Governance & Compliance: CloudTrail, Config, GuardDuty, Macie, Inspector vs. Azure Policy, Blueprints, Defender for Cloud, and Sentinel

GenAI IAM Security: enforcing Zero-Trust IAM roles and RBAC policies to restrict LLM API access

Git Fundamentals: version control concepts, local vs. remote repositories, committing, and log auditing

Advanced Git Operations: branching strategies (Trunk-Based vs. GitFlow), Pull Requests, Webhooks, Stash, Rebase, and resolving merge conflicts

Security & Cloud Integration: SSH/Personal Access Token authentication, secret detection with GitLeaks, and Git operations with Terraform across AWS and Azure

IaC Foundations: Declarative vs. Imperative IaC — AWS CloudFormation vs. Azure Bicep/ARM Templates vs. HashiCorp Terraform

Terraform Deep Dive: HCL syntax, Providers (AWS/AzureRM), Resources, Variables, Output values, Dynamic blocks, and Built-in Functions

State & Workspace Management: remote backends (S3+DynamoDB / Azure Blob Storage), state locking/migration, reusable modules, and Terraform Cloud

Image Automation & IaC Testing: baking AMIs/Managed Images with Packer, integrating with Terraform/GitHub/Jenkins, and security scanning with Checkov and tfsec

GenAI IaC Integration: provisioning complete AI stacks (Azure OpenAI, Bedrock, Vector DB instances, Private Link endpoints) via modular Terraform

Container Fundamentals: VMs vs. Containers, Docker architecture, multi-stage Dockerfile builds, volumes, custom networking, and Docker Compose

Container Security & Swarm: Docker Swarm clusters, Stacks, Secrets, Healthchecks, and vulnerability scanning with Trivy

Serverless Containers: AWS ECS/Fargate and Azure Container Instances (ACI)

Kubernetes Orchestration (AWS EKS & Azure AKS): Pods, Deployments, Services, Ingress Controllers, ConfigMaps, and Secrets

LLMOps Container Serving: packaging and deploying self-hosted LLMs (vLLM, Ollama) inside GPU-enabled Kubernetes clusters

Ansible Architecture: agentless architecture, inventory files, control nodes, and Ansible vs. Puppet vs. Chef

Ansible Playbooks & Security: Playbooks, Roles, Handlers, Jinja2 Templates, and encrypting parameters with Ansible Vault

Puppet Management: Puppet Master & Agent architecture, Manifests, Classes, and Modules

Azure Automation & DSC: Azure Automation Runbooks, Desired State Configuration, and hybrid management with AWS SSM and Azure Arc

GenAI FinOps Automation: automated Ansible playbooks and Python runbooks to scale down non-production GPU nodes off-hours for cost optimization

Build Systems & Artifact Management: Java Maven build lifecycles, POM files, and artifact publishing to JFrog Artifactory

Jenkins Pipeline Engineering: Master-Slave architecture, RBAC, Scripted vs. Declarative Pipelines, Multi-branch pipelines, SonarQube integration, and Slack notifications

Azure DevOps Ecosystem: Boards, Repos, Artifacts, Custom Agent Pools, Service Connections, YAML pipelines, and OIDC authentication with Azure/AWS

GitHub Actions Automation: custom workflows, matrix builds, secret injection, and OIDC integration with AWS/Azure Service Principals

DevSecOps Pipeline Security: SAST (SonarQube), secret scanning (GitLeaks), IaC scanning (Checkov), container scanning (Trivy), DAST (OWASP ZAP), and HashiCorp Vault

GenAI CI/CD Integration: pipelines that test LLM prompt outputs, run guardrail checks, and deploy updated model services

System Monitoring & Logging: AWS CloudWatch vs. Azure Monitor/Log Analytics/Application Insights, Prometheus metrics, and custom Grafana dashboards with Alertmanager

LLMOps Observability: tracking token consumption costs, prompt latency, API error rates, and evaluation metrics using LangSmith/Phoenix

Operations & Audit: auditing AWS API calls via CloudTrail/Config and tracking Azure Activity Logs and Defender for Cloud alerts

Production Capstone Project: building and deploying an Enterprise Multi-Cloud RAG Platform across AWS and Azure with Terraform, Kubernetes, and monitoring

Multi-Cloud End-to-End MLOps Pipeline with MLflow & EKS/AKS

Real-time Feature Store & Serverless ML Inference Platform

Self-Healing Kubernetes Cluster with AI Log Diagnostics & Remediation

AI-Powered Cloud FinOps & Anomaly Detection Engine

Automated GitOps PR Auditor & DevSecOps Guardrail

Prerequisites

What you should know or have before joining this program.

Basic familiarity with computers and the command line

No prior cloud or DevOps experience required — the course starts from Linux fundamentals

Interest in cloud infrastructure, automation, and platform engineering

Who Is This For?

This program is designed for the following learners.

Aspiring DevOps and cloud engineers entering the field

System administrators and developers wanting multi-cloud automation skills

Students and career switchers targeting DevOps, SRE, or platform engineering roles

Why study near Bangalore?

Why students from Bangalore choose IntellentX for Multi-Cloud (AWS + Azure) DevOps

Bangalore's technology market is the most competitive in India — and staying ahead requires more than a degree. IntellentX's Multi-Cloud (AWS + Azure) DevOps programme is built for Bangalore's professionals and students who want to transition into Multi-Cloud (AWS + Azure) DevOps roles or accelerate within their current track. The 4 Months programme is live, interactive, and project-driven — covering the tools, frameworks, and real-world scenarios that companies in Bangalore's product and service ecosystem actually hire for. Unlike generic online platforms, our small batch sizes ensure you get direct trainer interaction, code reviews, and personalised guidance throughout. Our placement cell has connected students with Multi-Cloud (AWS + Azure) DevOps opportunities at companies across Bengaluru, Hyderabad, Pune, and Chennai.

FAQs — Multi-Cloud (AWS + Azure) DevOps near Bangalore

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Multi-Cloud (AWS + Azure) DevOps Course near Bangalore, Hyderabad