SB
Spandana Bala
DevOps Engineer
Hyderabad · India
Now at · Microsoft
Class 014 · MULTI CLOUD DEVOPS · AWS + AZURE
Multi Cloud DevOps
& AI
Agents
Master end-to-end DevOps with Agentic AI Ops. Build Core DevOps with Linux, Jenkins, Docker, Kubernetes on AWS EKS, and Terraform, ship the Azure DevOps ALM stack, and deploy a Coding Agent that generates Shell, Dockerfiles, K8s manifests, and Terraform on demand.
3mo
duration
30+
modules
4.7/5
class rating
100k+
enrolled
Where our DevOps alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
What you leave with
Four things every Multi Cloud DevOps grad walks away with.
01
Agent-Ready DevOps skills
Both production clouds — AWS Solutions Architect depth and the full Azure DevOps ALM stack — plus Docker, Kubernetes, Terraform, Jenkins, GitHub Actions, Prometheus/Grafana, and Cursor AI for DevOps.
02
A shipped project
A production-deployed Coding Agent for Infra that generates Shell, Dockerfiles, K8s manifests, and Terraform modules via LangGraph + Claude Agent SDK + MCP, with a public verification URL.
03
Verifiable credential
2026 Agent-Ready rubric mapped to AWS SAA + Cloud Practitioner, AZ-400, CKA, Terraform Associate, and Docker Certified Associate, graded 1–5, with a public verification URL recruiters can check in 30 seconds.
04
Direct placement pipeline
GitHub + LinkedIn portfolio rewrite, DevOps-tuned resume rebuild, and warm intros into our 1,000+ hiring partners actively staffing DevOps, SRE, Cloud, and Platform Engineering roles.
3 MONTHS · FOUR PHASES · TWO CLOUDS · ONE CODING AGENT
From “SSHs into a server” to ships autonomous multi-cloud infrastructure..
Weeks 1–2 · Foundations + Linux
IT & AI Foundations + DevOps + Linux
- Application lifecycle, Agile/Scrum, and cloud computing (AWS + Azure)
- DevOps culture — the infinity loop and SDLC integration
- Networking basics — VPC, Security Groups, ports, and protocols
- Linux essentials and shell scripting for automation
YOU SHIPAn automated Bash deployment script that provisions an EC2 or Azure VM, configures networking, and brings up a working web service.
Weeks 3–8 · Core DevOps Engine (12 modules)
Git → Jenkins → Docker → Kubernetes → Terraform → Observability → Cursor AI
- Git, GitHub, GitFlow, and Jenkins CI/CD with SonarQube and Nexus
- Docker and Kubernetes on AWS EKS with Ingress and IAM
- Terraform IaC with multi-cloud providers and remote state
- Prometheus, Grafana, and GitHub Actions for observability and CI/CD
YOU SHIPA production Kubernetes platform on AWS EKS, Terraform-provisioned, observed by Prometheus/Grafana, and deployed via Jenkins + GitHub Actions.
Weeks 8–12 · Multi-Cloud + Python
Azure DevOps + AWS Cloud + Python for DevOps
- Azure Boards, Repos, Pipelines, Artefacts, and Test Plans
- AWS compute — EC2, Auto Scaling, ELB, Lambda, and API Gateway
- AWS networking, storage, and cost management with Trusted Advisor
- Python for DevOps — automation scripts and advanced programming
YOU SHIPA complete Azure DevOps pipeline running alongside your AWS infrastructure, with Python automation scripts handling repetitive ops work.
Weeks 12–15 · GenAI + Agentic AI
Master the 2026 GenAI + Agentic AI stack — and ship an infrastructure Coding Agent that generates Terraform, K8s manifests, and Dockerfiles on demand through MCP.
Engineer with LLM APIs from OpenAI, Anthropic, Google GenAI, and DeepSeek. Master prompt engineering (zero-shot, few-shot, CoT, ReAct) and context engineering — the 2026 frontier discipline. Build production RAG pipelines with ChromaDB and pgvector. Master the 2026 production agent stack — LangGraph 1.0 (#1 production default), Claude Agent SDK (#2 MCP-native), CrewAI (#3 multi-agent crews). Wire it all through the Model Context Protocol (MCP) — 200+ server implementations, 97M+ monthly SDK downloads. Final project — a deployed Coding Agent focused on infrastructure automation, with MCP servers exposing your AWS account, Azure subscription, and K8s clusters. The DevOps engineer’s force multiplier.
Partner orgs
(2026)52
Projects
deployed310+
→ Placement
offers88%
Course curriculum
Six sections. 50+ modules. Every one maps to something you'll ship.
01
Fundamentals of IT & AI
Foundational track building the conceptual bedrock for every DevOps engineer and
cloud architect — application lifecycle, Agile/Scrum, computing infrastructure,
AI/ML/Generative/Agentic AI fundamentals, and real-world digital systems. Sets the context for
everything that follows in the multi-cloud DevOps + AI engineering stack.
Application fundamentals — what applications are, their types, web architecture
Web Technologies — Frontend (HTML, CSS, JavaScript, React) and Backend (Python,
Java, Node.js)
Database Systems — SQL (PostgreSQL, MySQL) and NoSQL (MongoDB)
The seven SDLC phases — Planning, Analysis, Design, Implementation, Testing,
Deployment, Maintenance
How DevOps integrates and accelerates every phase of the SDLC
Understanding the SDLC is fundamental — DevOps doesn't replace it, it makes every
phase faster and more reliable
Methodology Evolution — Waterfall vs Agile, the Agile mindset
Popular frameworks — Scrum, Kanban, Extreme Programming (XP)
Scrum Roles — Product Owner, Scrum Master, Development Team
Scrum Events — Sprint Planning, Daily Scrum, Sprint Review, Sprint Retrospective
Scrum Artifacts — Product Backlog, Sprint Backlog, Increment deliverables
User Stories — Epics, Themes, Acceptance Criteria
Estimating user stories and managing backlogs with Google Sheets and Azure Boards
In modern DevOps, the Sprint cadence drives release cadence — fast sprints enable
fast deployments
CPU Technology — general-purpose computing, sequential operations, multi-core
parallel processing
GPU Technology — parallel processing for AI training, data processing, scientific
simulations
IaaS — Infrastructure as a Service: virtualised servers, storage, networking
(EC2, Azure VM)
PaaS — Platform as a Service: development and deployment environments
SaaS — Software as a Service: ready-to-use applications via web browser
As a DevOps engineer, you'll work across all three models — provisioning IaaS,
configuring PaaS, and integrating SaaS into pipelines
AI is reshaping DevOps practice in 2026 — from AI-generated infrastructure code
to autonomous agents that diagnose and remediate incidents
Machine Learning — algorithms that improve through experience
Deep Learning — neural networks for complex pattern recognition
Generative AI — systems that create new content, code, configurations
Large Language Models — LLMs that generate Terraform, Bash, Kubernetes manifests
on demand
Agentic AI — autonomous systems that plan, reason, act, and learn — the future of
infrastructure automation
Customer Relationship Management — CRM systems and the DevOps stacks behind them
Human Resource Management Systems — HRMS infrastructure patterns
Retail & E-Commerce — high-traffic e-commerce platforms and the DevOps challenges
of scale
Healthcare Applications — regulated workloads, HIPAA-compliant infrastructure
Real-world systems demonstrate why DevOps engineers are paid well — every
business now depends on reliable software delivery
02
Core DevOps & Advanced DevOps
The heart of the programme — 12 modules covering the entire DevOps lifecycle.
From booting your first Linux server through to provisioning production Kubernetes clusters on AWS
EKS, observed by Prometheus, deployed by GitHub Actions, and increasingly written by Cursor AI. By
the end of this section you can take a senior DevOps role at any major Indian GCC or product
company.
What is DevOps — culture, practices, and tooling working together
The DevOps infinity loop — Plan → Code → Build → Test → Release → Deploy →
Operate → Monitor
SDLC integration — how DevOps practices accelerate every phase
Servers — types, lifecycle, and management approaches
Cloud computing introduction — AWS and Azure as the two clouds you'll master
Networking basics — VPC, Security Groups, Ports, Protocols
Launching EC2 instances on AWS
Launching Azure VMs
Configuring basic networking and security groups
DevOps is 60% culture and process, 40% tools — most engineers obsess over the
tools and underweight the culture
Linux file system hierarchy — /etc, /var, /usr, /opt, /home, navigation patterns
Essential CLI commands — ls, cd, cp, mv, rm, find, grep, sed, awk
User and group management — useradd, groupadd, passwd, sudo
File permissions and ownership — chmod, chown, the octal permission model
Package management with APT on Ubuntu/Debian
Process management — ps, top, htop, kill, systemctl
Bash syntax — variables, conditionals, loops, functions
Standard input/output and redirection
Pipes and command composition
Exit codes and error handling
Cron jobs for scheduled automation
Hands-on — automated web server deployment script that provisions an EC2
instance, installs and configures NGINX, deploys a sample app, and verifies the service is running
Every production system has hundreds of Bash scripts holding it together
Working Directory, Staging Area, Repository — the three-zone model
Object model — blobs, trees, commits, refs
The .git directory — what's actually in there
git add, git commit, git push, git pull
git status, git log, git diff
git clone, git fetch, git remote
Branching strategies — feature branches, release branches, hotfix branches
GitFlow vs GitHub Flow vs Trunk-Based Development
Merge vs Rebase — when to use each
Resolving merge conflicts confidently
GitFlow is for projects with scheduled releases; GitHub Flow is for continuous
deployment — choose based on your release cadence
Repositories, issues, projects, discussions
Pull requests and code reviews — the discipline that catches 80% of bugs
GitHub webhooks for automation — triggering Jenkins, Slack notifications,
deployment events
GitHub Actions overview (deep dive in Module 11)
Branch protection rules and required reviews
Three-tier application architecture — Web tier, App tier, Data tier
Database setup and management with PostgreSQL
Backend development with Node.js and Express
Frontend development with ReactJS
Web server configuration with NGINX
Process management with PM2
Hands-on — complete LMS application deployment becomes the reference app you'll
containerise, orchestrate, monitor, and deploy through CI/CD pipelines
You can't DevOps what you don't understand — spend time on this module
The CI/CD flow — Code Commit → Static Analysis → Build → Test → Artifact → Deploy
Jenkins installation and initial configuration
Jenkins architecture — controller and agents
Freestyle jobs vs Pipeline jobs
Plugins ecosystem and management
Jenkinsfiles — pipeline-as-code
Pipeline syntax — pipeline, agent, stages, steps, post
Shared libraries for reusable pipeline code
Pipeline triggers — manual, scheduled, SCM polling, webhooks
GitHub webhook integration — automatic pipeline triggers on push and PR
SonarQube — static code analysis, quality gates, technical debt tracking
Nexus Repository — artefact management, dependency caching, release artefacts
Deployment to Docker, Kubernetes, AWS, Azure
Hands-on — complete Jenkins pipeline for the LMS with SonarQube scans, Nexus
artefacts, automated staging deployment
Jenkinsfiles in Git is the most underrated DevOps practice — version your
pipelines like you version your code
Containers vs Virtual Machines — the fundamental architectural difference
Monolithic vs Microservices architecture — why containers and microservices grew
together
Docker architecture — daemon, client, registry, images, containers
Working with Docker images and containers — pull, run, exec, logs, stats
Docker networking — bridge, host, overlay, custom networks
Data persistence with Docker volumes and bind mounts
Writing Dockerfiles — instructions, layers, caching
Multi-stage builds for optimisation — separating build environment from runtime
Docker Compose for multi-container applications
Pushing images to Docker Hub and private registries
Image security — scanning, signing, minimal base images
Hands-on — containerise the complete LMS application with Dockerfiles for
backend, frontend, database; Docker Compose orchestrating the full stack
The first time docker run works on your laptop AND in production with zero
changes, you understand why Docker won
Master and Worker node architecture — control plane components (API server,
scheduler, controller manager, etcd); worker components (kubelet, kube-proxy, container runtime)
Pods — the smallest deployable unit; multi-container pods; init containers
Deployments — managing replica sets, rolling updates, rollbacks
Services — ClusterIP, NodePort, LoadBalancer, ExternalName
ConfigMaps — externalising configuration from code
Secrets management — encrypted secrets, sealed secrets, external secret operators
Persistent storage — PersistentVolume, PersistentVolumeClaim, StorageClass
Ingress controllers — NGINX Ingress, AWS ALB Ingress
Network policies for pod-to-pod communication control
DNS in Kubernetes
Rolling updates — zero-downtime deployments
Rollbacks when things go wrong
Blue-green and canary deployment patterns
EKS cluster architecture — managed control plane, customer-owned data plane
Node groups — managed vs self-managed; scaling configuration
Cluster networking — VPC CNI plugin, pod IP allocation
AWS Load Balancer Controller — automatic ALB/NLB provisioning from Ingress
EKS networking and security — security groups for pods, network policies
ExternalDNS for automatic Route 53 record management
IAM roles for service accounts (IRSA) — pod-level AWS permissions
RBAC integration with AWS IAM
Cost optimisation strategies — spot instances, right-sizing, Cluster Autoscaler,
Karpenter
Cluster upgrades and version management
Foundation for the Certified Kubernetes Administrator (CKA) certification
Production K8s is 10% YAML and 90% operations — observability, cost, security,
upgrades
The Terraform Workflow — Write → Plan → Apply → Manage State
Write — HCL configuration files defining resources, variables, and outputs
Plan — preview changes before applying
Apply — provision infrastructure across AWS, Azure, and GCP with a single command
Manage State — Remote state storage with S3 + DynamoDB, workspaces, and module
reusability
HCL — resources, data sources, variables, outputs, locals
Expressions, functions, dynamic blocks
For-each and count for resource multiplication
Type constraints and validation
AWS provider — comprehensive AWS resource coverage
Azure provider — Azure resource management
GCP provider — Google Cloud resources
Multi-provider deployments — the full multi-cloud workflow
Modules for reusability — building your team's standard infrastructure library
Remote state storage with S3 + DynamoDB locking
Workspaces for environment separation
State management — terraform state commands, surgery, migration
Drift detection and remediation
Foundation for the HashiCorp Terraform Associate certification
Terraform state is the single most important file in your infrastructure —
protect it like a database
Metrics — numerical measurements over time (Prometheus territory)
Logs — discrete event records (Loki, ELK, CloudWatch Logs)
Traces — request flow through distributed systems (Jaeger, Tempo)
Prometheus architecture — server, exporters, alertmanager, pushgateway
Pull-based metric collection model
Service discovery — static, file-based, K8s
Data model — metrics, labels, samples
PromQL query language — selectors, operators, functions, aggregations
Storage — TSDB internals, retention, downsampling
AlertManager — alerting rules defining what to alert on and when
Routing rules and grouping
Inhibition and silences
Integration with PagerDuty, Slack, email, OpsGenie
Building Grafana dashboards — panel types, queries, variables
Data source configuration — Prometheus, Loki, CloudWatch, MySQL
Dashboard provisioning as code
Templating and dynamic dashboards
Monitoring Kubernetes with Prometheus — kube-prometheus-stack, node-exporter,
kube-state-metrics
Golden signals — latency, traffic, errors, saturation
SLI/SLO definition and error budgets
Hands-on — complete monitoring stack for LMS: deploy Prometheus, Grafana,
AlertManager on K8s; instrument the LMS; build dashboards; configure alerts
Workflow syntax and structure — YAML format, .github/workflows/
Events — push, pull_request, schedule, workflow_dispatch, repository_dispatch
Jobs — units of work running on a runner
Steps — individual commands or actions within a job
Conditional execution with if expressions
Job dependencies with needs
GitHub-hosted runners — Linux, Windows, macOS options
Self-hosted runners — for private VPCs, GPU workloads, custom environments
Runner labels and routing
Matrix builds for parallel execution across versions, platforms, environments
Reusable workflows with workflow_call
Composite actions for shared logic
Secrets and environment management
OIDC integration with cloud providers (no long-lived credentials)
Docker image building and pushing — multi-arch builds, signed images
Kubernetes deployment automation — kubectl, Helm, ArgoCD integration
AWS deployment patterns — ECR push, EKS deploy
Azure deployment patterns — ACR push, AKS deploy
Hands-on — complete GitHub Actions pipeline for LMS: on push: build, test, scan,
push to ECR, deploy to EKS, smoke test, post Slack notification
GitHub Actions vs Jenkins isn't either/or — most enterprises run both
AI pair programming for infrastructure engineers — the skill that quietly
differentiates senior candidates in 2026
Shell scripts — describe what you need in plain English; Cursor writes the Bash
with complex parsing, error handling, and retry logic
Dockerfiles with AI — multi-stage builds generated with security best practices
Optimised layers — Cursor knows the cache patterns
Security best practices — minimal base images, non-root users, secret handling
Kubernetes manifests — Deployments, Services, Ingress, HPA generated as
production-ready YAML
Health checks, resource limits, security contexts all included
ConfigMaps and Secrets wired correctly
Terraform code generation — complete modules, variable files, and state
configurations
Provider blocks, backend configuration, module composition
Prompt engineering for infrastructure code
Context management — feeding the right files to the AI
Validation patterns — never trust, always verify
When to use Cursor vs Claude Code vs GitHub Copilot
Pair programming workflows with AI
In 2026, the question is no longer "Do you use AI for DevOps?" but "How
effectively do you use AI for DevOps?"
03
Azure DevOps (ALM)
The complete Microsoft Application Lifecycle Management stack — used by
enterprises worldwide and a hard requirement for many GCC and large-enterprise roles in India. While
Section 2 covered Jenkins and GitHub Actions, this section gives you the Microsoft alternative — and
the dual-cloud capability that doubles your job market.
Agile project management — Scrum, Kanban, CMMI templates
Work items — User Stories, Tasks, Bugs, Features, Epics
Sprints — planning, capacity management, burndown charts
Boards — Kanban-style visualisation, swimlanes, WIP limits
Queries — filtering and reporting on work items
GitHub integration — link commits and PRs to work items
Dashboards — team and stakeholder visibility
Azure Boards is the GCC standard for backlog management — if you're targeting
JPMorgan, Goldman, HSBC, or similar, you need this
Source control — Git repositories with full feature parity to GitHub
Branching strategies — Git branch policies, branch protection
Cloning repositories with Git CLI and Azure DevOps integration
GitHub import — migrate repositories from GitHub to Azure Repos
Branch policies — required reviewers, build validation, work item linking
Pull request workflows in Azure DevOps
Azure Repos and GitHub interoperate well — many enterprises use both, with Azure
Repos for sensitive code and GitHub for OSS
CI/CD with YAML — pipeline-as-code, version-controlled alongside source
Triggers — CI triggers, PR triggers, scheduled triggers, manual triggers
Agents — Microsoft-hosted vs self-hosted agents, agent pools
Deployment strategies — Rolling, Blue-Green, Canary
Templates and reusable pipeline components
Variable groups and Azure Key Vault integration
Stages, jobs, steps — the hierarchy
Approvals and gates for production deployments
Multi-stage pipelines — Build → Test → Deploy to Dev → Staging → Production
Environment-specific configurations
Manual approval gates for production
Foundation for the Microsoft AZ-400 Azure DevOps Engineer Expert certification
Azure Pipelines, GitHub Actions, and Jenkins are 80% the same conceptually — once
you've mastered one, the other two come quickly
Package management — public and private packages
Feeds — package repositories with access control
Views — promotion across @local, @prerelease, @release
Pipeline integration — publish from Pipelines, consume in builds
Upstream sources — proxying public registries (npm.org, NuGet.org, Maven Central)
Retention policies for cost management
Artefact management is invisible until you don't have it — then your CI/CD
becomes painfully slow because every build re-downloads dependencies from the internet
Test planning — test plans, test suites, test cases
Test execution — manual testing with rich reporting
Test tracking — pass/fail rates, defect linking, traceability
Web app testing — exploratory testing, screen capture, video recording
Integration with Azure Boards for defect tracking
Integration with Azure Pipelines for automated test execution
Test analytics and reporting
Test Plans is the part of Azure DevOps most engineers ignore — but for regulated
industries (BFSI, healthcare), the auditable test execution it provides is gold
04
Python for DevOps
Bash gets you started; Python gets you scaled. This section gives DevOps
engineers the Python fluency needed for serious automation — boto3 for AWS, azure-sdk for Azure,
custom CLI tools, infrastructure validation scripts, and AI agent integration. Five modules focused
specifically on the DevOps use cases for Python.
Python interpreter installation and PATH configuration
Virtual environments — venv, pip, requirements.txt
Python 3.12+ features relevant to DevOps work
Variables and data types
Control flow — if, for, while, comprehensions
Functions, parameters, return values
String formatting — f-strings for log messages and command building
Lists, tuples, dictionaries, sets — the four pillars
List/dict comprehensions for elegant data transformation
Working with nested structures (the shape of YAML and JSON data)
Reading and writing text files
JSON — json.load, json.dump, parsing API responses
YAML — pyyaml library, parsing K8s manifests and CI configs
TOML — tomli for parsing configuration files
Working with CSV files for inventory data
Path manipulation with pathlib
Function design — single responsibility, clear inputs/outputs
*args and **kwargs for flexible signatures
Default arguments and keyword-only arguments
Type hints for self-documenting code
Built-in modules — os, sys, subprocess, shutil, pathlib
Creating reusable modules for your team
Package management with pip
Building internal Python tools
try/except/else/finally blocks
Catching specific exceptions
Custom exception classes
Logging best practices with the logging module
DevOps scripts run unattended — robust error handling and logging aren't
optional; they're survival
boto3 — the AWS SDK for Python
Authentication patterns — IAM roles, profiles, assumed roles
Core services automation — EC2, S3, IAM, Lambda, ECS, EKS
Pagination and retry patterns
Real-world scripts — cost reports, instance inventory, security audits
azure-sdk-for-python — the Azure SDK
Authentication with DefaultAzureCredential
Core services automation — VMs, Storage, Resource Manager
Multi-cloud abstraction patterns
requests library for HTTP
Authentication patterns — Basic, Bearer tokens, OAuth2
Webhook handling
Rate limiting and retry with exponential backoff
click or typer for building professional CLI tools
Argument parsing, subcommands, help text
Building tools your team will actually use
Decorators — for logging, timing, retry, authentication
Context managers — with statement for safe resource management
Generators — memory-efficient processing of large infrastructure inventories
Concurrency — concurrent.futures for parallel API calls
pytest for unit tests
Mocking AWS/Azure APIs with moto and similar libraries
Integration testing infrastructure code
setuptools and pyproject.toml
Building internal Python packages
Private PyPI servers (Nexus, Artifactory)
Driving Terraform programmatically
Building custom Kubernetes operators in Python
AI agent integration (preview of Section 6)
The Python you write here will be the foundation for the agentic infrastructure
work in Section 6
05
AWS Cloud Computing
Five modules covering AWS at the depth required for both AWS Solutions Architect
Associate and AWS Cloud Practitioner certifications. While Section 2 covered EKS specifically, this
section gives you the broader AWS fluency — compute, networking, storage, security, and cost
management — needed for any cloud-engineer role.
AWS Global Infrastructure — Regions, Availability Zones, Edge Locations
Service availability across regions
Pricing models — On-Demand, Reserved, Spot, Savings Plans
AWS Free Tier and learning patterns
IAM Users, Roles, Policies — the AWS security backbone
Identity-based vs resource-based policies
IAM best practices — least privilege, MFA, key rotation
IAM roles for service accounts (cross-references EKS module)
AWS Organizations and SCPs for multi-account governance
AWS CLI setup and configuration — profiles, credentials, region
Core CLI commands across services
Output formatting — JSON, table, text
Scripting with the CLI
CloudWatch — metrics, logs, alarms, dashboards
CloudTrail — audit logging for all API calls
AWS Config for compliance and resource tracking
Core content for AWS Cloud Practitioner and foundational for AWS Solutions
Architect Associate
EC2 instance types and families — General Purpose, Compute, Memory, Storage, GPU
optimised
Instance pricing models — On-Demand, Reserved, Spot, Dedicated
AMIs (Amazon Machine Images) — building, sharing, marketplace
Instance metadata service (IMDSv2)
Security groups and key pairs
Auto Scaling Groups (ASGs) — launch templates, scaling policies
Target tracking, step scaling, scheduled scaling
Lifecycle hooks for graceful instance management
Integration with Application Load Balancer
Application Load Balancer (ALB) — Layer 7 HTTP/HTTPS routing
Network Load Balancer (NLB) — Layer 4 for extreme performance
Gateway Load Balancer (GWLB) — for third-party security appliances
Target groups, health checks, sticky sessions
AWS Lambda — serverless compute, event-driven
Lambda execution model, concurrency, cold starts
Lambda integrations — API Gateway, S3, DynamoDB, EventBridge
Amazon API Gateway — REST, HTTP, WebSocket APIs
Authentication and authorisation for APIs
Lambda is for event-driven workloads under 15 minutes; EC2 is for everything else
— pick the right tool
VPCs, Subnets, Route Tables — the foundational networking layer
Public vs private subnets and the routing implications
Internet Gateway vs NAT Gateway vs NAT Instance
VPC endpoints (Interface vs Gateway) — private connectivity to AWS services
VPC peering and Transit Gateway for multi-VPC architectures
Security Groups (stateful) vs Network ACLs (stateless)
AWS KMS — Key Management Service for encryption at rest
Customer-managed keys (CMK) vs AWS-managed keys
Encryption envelopes — data keys and master keys
Amazon Cognito — User Pools and Identity Pools for application authentication
Cognito federations with social providers
Foundation for AWS Solutions Architect Associate — VPC questions are heavily
weighted
S3 buckets and objects — the object storage that started it all
Bucket permissions — bucket policies, ACLs, S3 Block Public Access
Storage classes — Standard, IA, One Zone, Glacier, Deep Archive
Lifecycle policies for cost optimisation
Versioning and MFA Delete
S3 events for serverless triggers
Cross-region replication
EBS volumes — block storage for EC2
Volume types — gp3, gp2, io2, io1, st1, sc1
Snapshots and backup strategies
Multi-Attach for shared volumes
Encryption at rest with KMS
EFS — Elastic File System for shared file storage
FSx — managed file systems (Windows, Lustre, NetApp)
AWS Backup for centralised backup management
S3 is one of the most exam-heavy services on AWS certifications — know storage
classes, lifecycle policies, and permissions inside out
AWS Cost Explorer — historical analysis and forecasting
AWS Budgets — alerting on spend thresholds
Cost allocation tags for chargeback
Cost and Usage Reports (CUR) for detailed analysis
AWS Trusted Advisor — automated cost, security, and performance recommendations
AWS Compute Optimizer for right-sizing
Cost Anomaly Detection
Savings Plans — Compute Savings Plans vs EC2 Instance Savings Plans
Reserved Instances — Standard vs Convertible
Spot Instances for fault-tolerant workloads
Right-sizing campaigns
Cost management is heavily tested on AWS Cloud Practitioner and increasingly on
Solutions Architect exams
The fastest career move for a junior DevOps engineer is to find $50K of waste in
their org's AWS bill — instant promotion candidate
06
Generative AI & Agentic AI
The production AI engineering destination — and where this programme
distinguishes itself from every other Indian DevOps course. From the 70-year arc of AI history to
deploying a production Coding Agent for Infrastructure — this section builds the complete 2026 GenAI
engineering stack tuned for DevOps work: frontier models, prompt engineering, RAG, agent frameworks,
and the Model Context Protocol. The named Coding Agent project lives here.
Narrow AI — image classifiers, speech recognition — the pre-2022 era
Generative AI — LLMs, image/video/audio generation — the post-2022 era unleashed
by ChatGPT
Agentic AI — Plan / Reason / Act / Learn loops, tool use — the post-2024 era of
autonomous systems
2022 inflection point — ChatGPT launch enters mainstream consciousness
2024 inflection point — AI systems begin planning, using tools, completing
multi-step tasks autonomously — including DevOps tasks
What's coming 2026-2030 — increasingly capable reasoning models, deeper tool
integration with infrastructure systems
Multi-agent collaboration at scale and systems that learn continuously from
production feedback
For DevOps engineers, the agentic era means agents that can investigate
incidents, draft Terraform changes, and propose remediations — with you reviewing and approving
GPT-5.5 — The Autonomous Agent. Terminal-Bench 2.0 leader at 82.7%,
OSWorld-Verified at 78.7%
GPT-5.5 critical for DevOps — best model at running CLIs and SSH sessions
Claude Opus 4.7 — The Precision Coder. Hybrid reasoning with extended thinking
mode
Claude Opus 4.7 — SWE-bench Pro leader at 64.3%, lowest hallucination rate at
36%, deepest native MCP support for DevOps tool integration
Gemini 3.1 Pro — The Context Giant. Natively multimodal with 2M+ token context
window large enough to ingest entire infrastructure codebases
Open-source frontier — Llama 4 (Meta), DeepSeek, Mistral, Qwen — when you need to
run models in your own VPC
Intelligent Routing for DevOps — Opus 4.7 as daily driver for Terraform and K8s
YAML generation
GPT-5.5 for autonomous incident response and terminal automation
Gemini 3.1 Pro for ingesting massive log files and entire codebases
Copilot Studio for building custom DevOps agents on Azure
GitHub Copilot in your IDE for code generation
Perplexity — citation-grounded research for AWS/Azure documentation
NotebookLM — long-document analysis for compliance audits
Fundamentals — Context + Task + Examples + Format + Constraints
Core Techniques — Zero-shot, few-shot, Chain-of-Thought (CoT), ReAct,
Tree-of-Thought
System Prompts — persistent persona design, guardrails, extended thinking
Multimodal — reading architecture diagrams, log screenshots
Hallucination & Context — grounding for accurate infrastructure code
Domain & Library — DevOps-specific prompt patterns + versioned prompt library
Context Engineering — managing what enters the LLM's context window — for DevOps
this means feeding the right Terraform modules, runbooks, and architecture docs at the right
moments
Project — ship a 30+ prompt library for DevOps work on GitHub
Using ChatGPT, Claude, and Gemini for daily DevOps work
AI for documentation — runbooks, architecture decision records, incident reports
Microsoft Copilot in Word, Excel, PowerPoint for status reports and architecture
diagrams
AI for code — GitHub Copilot, Cursor, Claude Code, ChatGPT Codex (Cursor covered
deeply in Section 2 Module 12)
Building DevOps-specific AI workflows that save 10+ hours per week
Reading architecture diagrams with vision models
Analysing screenshot of logs and errors with multimodal LLMs
OCR for legacy infrastructure documentation
Image generation for architecture documentation
Video — extracting information from incident replay recordings
The "I'll send you a screenshot of the error" workflow now works perfectly —
multimodal LLMs read screenshots better than humans
Hallucination — why it happens, and how it's catastrophic when an LLM
hallucinates a Terraform resource name
Prompt injection — when an attacker poisons your AI's context (especially in
agentic workflows reading external content)
Privacy — keeping infrastructure code, AWS account IDs, customer data out of
public LLMs
Security — secrets management when AI tools have IDE access
Regulatory landscape — EU AI Act, India DPDP Act
Human-in-the-loop — when AI proposes infrastructure changes, you approve
An AI that hallucinates a non-existent AWS API and your apply succeeds locally
but fails in prod is the new "works on my machine"
Streamlit — rapid prototyping for internal DevOps dashboards
FastAPI — production-grade Python API for AI services running in your cluster
Building chatbots for runbook lookup
Building diagnostic agents for common infrastructure problems
Deploying internal AI tools to your team
Build and deploy a Streamlit + FastAPI internal tool that answers DevOps
questions from your team's documentation
LLM APIs in production — OpenAI, Anthropic, Google GenAI, DeepSeek Python SDKs
API patterns — completions, chat, streaming, function calling, structured outputs
Rate limits, retries, exponential backoff
Cost tracking and observability for AI API spend
Function calling & structured outputs — the 2026 production pattern for reliable
JSON
Pydantic-validated structured outputs — type-safe AI
Use cases — extracting structured data from logs, generating valid Terraform JSON
Embeddings — OpenAI text-embedding-3-large, Voyage, Cohere models
Vector databases — ChromaDB (local/dev), Pinecone (managed), Qdrant
(open-source), pgvector (PostgreSQL)
Indexing strategies — HNSW, IVF
RAG pipeline — Chunk → Embed → Index → Retrieve → Augment → Generate
Building RAG over your runbooks, architecture docs, post-mortems
Hybrid search (BM25 + embeddings) for technical documentation
Re-ranking with cross-encoders
Agentic RAG — self-improving retrieval where the agent decides if it has enough
infrastructure context
Multi-step retrieval — first find the right service, then the right runbook, then
the right command
Project — Internal RAG App for DevOps Docs: RAG over runbooks, post-mortems,
architecture docs; deployed to K8s with hybrid search, re-ranking, ChatOps Slack integration
LangGraph 1.0 — complex stateful workflows, graph-based state machines,
human-in-the-loop, LangSmith observability — the production default for agentic DevOps
Claude Agent SDK — powers Claude Code, deepest MCP integration critical for
DevOps tool calls, extended thinking for complex remediation planning
CrewAI — role-based multi-agent crews, fastest prototyping; use case: a "DevOps
team" of agents (Architect, Developer, SRE, Security Reviewer)
Semantic Kernel / Microsoft Agent Framework — enterprise .NET stacks, common in
Microsoft-heavy GCCs
Pydantic AI — type-safe Python, validation-first agent design
ReAct (Reasoning + Acting) — investigate, then act
Plan-and-Execute — generate a multi-step remediation plan, then execute
Reflection loops — agent reviews its own infrastructure change before applying
Multi-agent collaboration — Architect proposes, Reviewer critiques, SRE deploys
Human-in-the-loop checkpoints — humans approve every production-touching action
Production DevOps agents are 90% about state management, observability, and human
approval gates — never deploy an agent without explicit approval steps for destructive operations
MCP — the open standard for connecting agents to tools, data, and systems
Proposed by Anthropic in late 2024, now stewarded by the Linux Foundation
200+ server implementations and 97M+ monthly SDK downloads
Adoption across Anthropic, OpenAI, Google, Microsoft, AWS, and 50+ partners
For DevOps — MCP servers exist for AWS, Kubernetes, GitHub, GitLab, Terraform,
and dozens more
Build an MCP server exposing AWS resources with IAM-aware authorization
Build an MCP server exposing a Kubernetes cluster for safe pod/service inspection
Build an MCP server exposing Terraform state for safe infrastructure inspection
Connect LangGraph agents to multiple MCP servers via adapters
Use Claude Agent SDK's deepest native MCP integration
A2A Protocol — Google-led agent-to-agent communication standard with 50+ launch
partners including Salesforce, SAP, Anthropic, Cisco
Linux Foundation governance
Three state management levels — session-level, agent-level, task-level
CODING AGENT CAPSTONE — multi-agent infrastructure Coding Agent using LangGraph +
Claude Agent SDK with MCP servers exposing your AWS account, Azure subscription, Kubernetes
clusters, and Terraform state
Agent generates Shell scripts, Dockerfiles, K8s manifests, and Terraform modules;
proposes infrastructure changes; runs plan; waits for human approval; applies
React frontend, FastAPI backend, observability via LangSmith — the named project
for the entire Multi Cloud DevOps & AI Agents programme
Tools you'll master
32+ DevOps & AI Ops tools, one production project.
Real-time projects
You don't watch videos. You ship software.
Three full-production projects, each threaded through the entire curriculum. By the project, you've built the whole stack around them.
Hero project · weeks 3–12
Production multi-cloud platform with AI Ops
Build an end-to-end platform across AWS, Azure, and GCP — Terraform-baked infrastructure, ArgoCD-managed Kubernetes, Prometheus + Grafana + Loki + Tempo observability, and a LangGraph AI Ops agent that triages alerts and drafts post-mortems for the on-call SRE.
01Multi-cloud GitOps pipeline — Terraform + Packer baselines for AWS/Azure/GCP, ArgoCD-managed K8s manifests, branch-based environments, signed commits and policy-as-code.
02K8s platform with golden paths — Helm/Kustomize charts, secrets via Vault, service mesh, autoscaling, multi-region failover, blue/green + canary rollouts.
03Observability stack — Prometheus + Grafana + Loki + Tempo, SLOs and error budgets, on-call runbooks in Confluence/Notion, Datadog APM for app teams.
04AI Ops agent — a LangGraph agent that triages alerts, drafts post-mortems, suggests rollbacks, and exposes runbooks via an MCP server to incident responders.
Outcome: ~70% MTTR reduction
Deploy success: 99.95%
Reviewer: Senior SRE panel
TerraformK8sArgoCDPrometheusLangGraph
Enterprise · weeks 6–11
Cloud cost & FinOps agent
Wire AWS / Azure / GCP cost APIs into a daily reporting agent — anomaly detection on spend, recommendation engine for right-sizing, Slack alerts with AI-drafted business context.
FinOpsAWSAzureLangChain
Real-time · weeks 8–12
Self-healing K8s incident loop
Build a Prometheus → AlertManager → LangGraph triage agent → ArgoCD rollback loop with hand-off to human SREs only when the agent's confidence is below threshold.
K8sLangGraphArgoCDSelf-healing
Project · weeks 11–12
Your AI DevOps platform in a real partner org.
Pick a real partner platform team. Deploy a production multi-cloud GitOps + observability + AI Ops stack — Terraform infrastructure, ArgoCD pipelines, Prometheus stack, LangGraph triage agent — into a partner team that's running it for real users.
Download the real world project
Full scope, sample partner orgs, weekly milestones, and grading rubric — PDF, 14 pages.
2026: 220+ deployed76% → placement offers
Your instructor
Taught by the engineer who ran your dream job's pipelines.
MK
Manikanta Kona
Founder, Digital Lync · Principal DevOps Architect
AWS · Azure · GCP · Kubernetes · Terraform · ArgoCD · LangGraph · MCP
"A 2026 DevOps engineer doesn't just write Terraform and Helm charts. They run a
multi-cloud platform with GitOps rigor, ship a LangGraph AI Ops agent that triages alerts at 3am, and stake
an SLA on the whole stack — Prometheus SLOs, ArgoCD rollbacks, MCP tool policies and all. That's the bar I
teach to, every cohort."
15 yrs
PLATFORM ENG
2,400+
LEARNERS
4.9 /5
RATING
Manikanta is the founder of Digital Lync and brings 15 years of multi-cloud platform engineering from AT&T, Salesforce, Cox Communications, and Broadcom — where he led GitOps adoption, Kubernetes platform builds, and cloud cost programmes for Fortune-500 banks, telcos, and insurers. Most recently he architected production AI Ops practices that pair Prometheus + Grafana observability with LangGraph triage agents and an MCP tool layer the on-call SRE team actually trusts in production.
His classes get you two things other programs don't give you: a founding architect who still ships production platforms, and a curriculum rewritten every quarter to match what hiring managers actually ask about — including multi-cloud GitOps, ArgoCD-driven rollouts, and AI Ops agents that operate alongside human SREs. M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, Digital Lync · Platform Engineering & AI Ops Lead
K8s · Terraform · ArgoCD · Prometheus · LangGraph · MCP · Multi-cloud
"A multi-cloud production platform stops being a slide when you stake an SLA on it —
when GitOps pipelines, Prometheus SLOs, ArgoCD rollouts, and a LangGraph triage agent are the way the
on-call SRE actually works on a Tuesday at 3am. GitOps rigor and AI Ops adoption in incident response aren't
optional anymore. That's what I teach."
10 yrs
PLATFORM ENG
1,800+
LEARNERS
4.8 /5
RATING
Ravi is Chief Technologist at Digital Lync, where he leads the platform engineering and AI Ops practice. After ~10 years building production multi-cloud Kubernetes platforms across enterprise teams, he stepped into the Chief Technologist seat to wire Terraform, ArgoCD, Prometheus, LangGraph, and MCP into the way SRE teams actually work — GitOps pipelines tuned to real incident patterns, MCP tool policies on-call engineers trust, observability with SLOs that matter, and AI Ops agents wired into incident response.
His AI Ops modules are built from real production post-mortems, not slide decks. Expect to leave with working Terraform multi-cloud baselines, ArgoCD GitOps pipelines, a Prometheus + Grafana + Loki + Tempo observability stack, an MCP server with auth + scope policy, and a LangGraph triage agent you can stake an SLA on. Hyderabad-based, hands-on, and known for the unglamorous parts of platform engineering that everyone else skips.
HIRING PARTNERS · INDUSTRY VOICES
What DevOps employers say about Digital Lync grads.
Real feedback from platform and SRE leaders at AI-first companies and the firms hiring our Multi Cloud DevOps + AI Ops graduates.
Digital Lync grads ramp 40% faster on platform rollouts than typical DevOps hires. Best Multi Cloud DevOps + AI Ops pipeline in India.
Aakash Mehta, SRE Director, Microsoft
We've onboarded 80+ Digital Lync alumni in 18 months. Lowest ramp time we've seen for GitOps pipelines and AI Ops triage practices.
Anita Sharma, Senior Manager, Deloitte
The Multi Cloud DevOps programme is comprehensive — Terraform, K8s, ArgoCD, AI Ops. Grads come pre-trained for production multi-cloud platform engineering.
Rahul Bhatt, Solutions Lead, Mphasis
Their GitOps + AI Ops track produces PMs who ship production-grade pipelines on day one. Rare combination of SRE rigor and platform craft.
Deepak Pillai, Senior Architect, TCS
What sets Digital Lync apart is the AI Ops layer baked into the DevOps track. Our enterprise clients ask for exactly this profile.
Suresh Menon, Practice Lead, Accenture
Their AWS Solutions Architect + CKA prep is rigorous, and the shipped project — GitOps pipeline, observability stack, AI Ops agent — is what closes interviews for us.
Vikram Iyer, Director, Infosys
Digital Lync's DevOps engineers ship reliable platforms twice as fast in the first 90 days. Our internal platform metrics back this up clearly.
Lakshmi Nair, VP Engineering, Wipro
Best Multi Cloud DevOps + AI Ops pipeline we've sourced from in India. Their projects are real shipped pipelines, not slide demos.
Karthik Subramanian, Engineering Director, Cognizant
Strong multi-cloud and Kubernetes foundation. Their DevOps grads need almost zero ramp time on enterprise platform engagements with us.
Arun Joshi, Practice Director, Capgemini
We've placed 40+ Digital Lync alumni across our platform and watsonx engineering teams. Strong fundamentals, sharp on SLOs and on-call.
Sanjay Verma, Talent Director, IBM
GitOps + AI Ops triage is exactly the talent gap we've been struggling to close. Digital Lync is filling it for us reliably.
Anjali Desai, Practice Head, LTIMindtree
Their DevOps track delivers engineers who navigate Terraform, K8s, and ArgoCD on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Hired 25+ Digital Lync graduates for our platform engineering practice. Strong on Terraform, sharp on K8s, fluent in AI Ops.
Geetha Pillai, Talent Acquisition Lead, Cyient
Digital Lync grads who blend platforms with Azure OpenAI Ops agents land production-ready on day one. Rare combination, well-trained.
Priya Reddy, Talent Lead, Microsoft
03Program certifications
An Agent‑Ready credential, not a participation trophy.
Digital Lync · Institute Certificate
Agent‑Ready DevOps Engineer
Presented to
Spandana Bala
For the successful design, build, and production deployment of a multi-cloud platform —
GitOps pipeline, K8s platform, observability stack, and an AI Ops triage agent — evaluated against the AWS Solutions Architect, CKA (Certified Kubernetes Administrator), and Terraform Associate credential rubrics.
Manikanta Kona
CEO · Digital Lync
AGENT
READY
2026
READY
2026
01
Industry‑recognized
Co‑branded with the platform engineering community and mapped to AWS Solutions Architect
and CKA (Certified Kubernetes Administrator) credentials — names that hiring managers already scan for on resumes.
02
Project artifact included
Every certificate carries your shipped project — GitOps pipeline, K8s platform, observability stack, AI Ops agent — with
a link to the live partner-org deployment. Proof, not a promise.
03
Enhanced skill validation
Graded against the 2026 Agent‑Ready rubric: GitOps pipelines, K8s platforms, observability stacks, AI Ops triage,
FinOps & SLOs. No pass/fail — a level 1‑5 band.
04
Verifiable on a public URL
Each credential has a public verification page recruiters can check in 10 seconds — no PDF
back‑and‑forth.
04Job placement support
Your first DevOps offer isn't a lottery ticket. It's a built process.
GitHub, LinkedIn, resume — and most importantly, warm intros into platform teams at AI-first companies. Our
placement team works your search like an account, not a helpdesk.
01 / GITHUB & PORTFOLIO
A portfolio, not a graveyard.
Guidance on building a portfolio that showcases your GitOps pipeline, K8s platform, observability dashboard, AI Ops agent, and a public verification URL — reviewed 1:1, not via template.
02 / RESUME PREP
Rewrite, don't proofread.
A one-page resume rebuilt around the platforms you shipped (GitOps pipelines, K8s clusters, AI Ops agents), the partner-org project, and the business outcome. Reviewed by platform leaders who've read 10,000+ resumes.
03 / LINKEDIN + INTROS
Where most opportunities actually live.
Profile tuning plus direct warm introductions into platform teams at AI-first companies — Microsoft, AWS, HashiCorp, Datadog, GitLab, Atlassian, Anthropic, Hugging Face, Databricks, Snowflake, Stripe, Razorpay, Freshworks, Zoho, plus services that staff platform teams (Deloitte, Accenture, Cognizant, TCS). You leave with recruiter contacts, not a generic "good luck."
DevOps alumni
Hundreds of DevOps careers launched — here are eight.
NV
Naveen Vedala
Senior SRE
Hyderabad · India
Now at · Atlassian
TA
Tejashwini Addla
Staff Platform Engineer
Hyderabad · India
Now at · Salesforce
TD
Tharunesh Dillikar
Principal DevOps Engineer
Seattle · United States
Now at · Datadog
MM
Mujahed Mohammed
Cloud Architect
Hyderabad · India
Now at · Databricks
BK
Bhargav Kumar Murala
Kubernetes Lead
Hyderabad · India
Now at · Adobe
SL
Sai Manasa Leburi
AI Ops Lead
New York · United States
Now at · Hugging Face
RD
Rahul Dhamma
Director of Platform Engineering
Hyderabad · India
Now at · HashiCorp
Our locations
Come chat with us — over coffee, or over Zoom.
One flagship campus in Hyderabad, plus online Principal DevOps Engineer cohorts running on Indian and US timezones.
Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave ·
Hyderabad, Telangana
Call
+91 90003 29956
US desk
+1 858 666 6719
Hours
Mon–Sat · 9am–9pm
Online class
Global
Weekend and evening DevOps cohorts running on IST and PST. Every online cohort ships
the same shipped project — GitOps pipeline, K8s platform, observability stack, AI Ops agent — as the on‑campus track.
Timezones
IST & PST
Format
Live + 1:1 mentorship
Next class
25 May 2026
FAQ
Questions we actually get — answered honestly.
Straight answers on prerequisites, the DevOps stack, certifications, and placement. If something's missing, book a 20-minute advisor call — no slides, no pitch.
Do I need a CS background or prior cloud experience?
No on both counts. Roughly 40% of every class comes from non-CS streams — mechanical, electrical, BCom, BBA, sysadmins, and self-taught coders. Weeks 1–2 cover the Linux fundamentals, networking, and Git basics from scratch. What you do need: consistency and 12–15 hours a week.
Will I actually run a production platform, or only do tutorials?
You actually run it. Every learner stands up a multi-cloud GitOps pipeline (Terraform on AWS/Azure/GCP), a Kubernetes platform with ArgoCD-managed deploys, a Prometheus/Grafana/Loki/Tempo observability stack, and a LangGraph AI Ops triage agent. The project runs on a partner platform — not a tutorial.
Which tools, clouds, and AI models will I use?
Clouds: AWS, Azure, GCP. IaC: Terraform, Packer, Ansible, Vault, Consul. K8s: Docker, Kubernetes, Helm, Kustomize, ArgoCD, Flux. CI/CD: Jenkins, GitHub Actions, GitLab CI, CircleCI. Observability: Prometheus, Grafana, ELK, Datadog, New Relic, Loki, Tempo, Jaeger. AI Ops: OpenAI, LangChain, LangGraph, MCP.
Will I prep for AIPMM DevOps Engineer and Pragmatic Principal DevOps Engineer certs?
Yes. The curriculum is mapped to the AIPMM DevOps Engineer track and the Pragmatic Principal DevOps Engineer credential. We run two full mock exams and reimburse the voucher fee on first-attempt pass.
What's the time commitment per week?
Plan for 12–15 hours: 2 live classes × 2 hours, 1 lab × 3 hours running your training cluster, and ~5 hours of project work (Terraform, K8s, observability). Saturday office hours with the TA team are optional, but most learners use them.
Is placement support really 1:1, and which companies hire DevOps engineers?
Yes — a dedicated placement advisor from week 8, not a helpdesk. AI product hiring partners include Microsoft, Adobe, Salesforce, Atlassian, Notion, Linear, Anthropic, Hugging Face, Databricks, Snowflake, Stripe, Razorpay, Freshworks, Zoho, and Postman. Resume, LinkedIn, mock interviews, and warm intros are individual.
Online, weekend, or on-campus?
All three. On-campus at the Hyderabad flagship, live online (IST and PST cohorts), and a weekend track for working professionals. Every format ships the same shipped project — GitOps pipeline, K8s platform, observability stack, AI Ops agent — only the schedule changes.
What if I fall behind, or can't continue mid-class?
Freeze your seat for up to 90 days and rejoin the next class — no extra fee. TAs run catch-up sessions every Saturday for anyone more than a week behind, and recordings of every live session are available for the lifetime of your account.
Still have a question? Talk to an advisor — no slides, no pitch.
Class DEV-028 starts 12 May 2026.
48 seats. 11 already claimed.
Book a 20-minute advisor call. We'll walk through the curriculum, match it to your current role, and show you two real projects from class 014.
