Learn. Build. Deploy. Scale.

Cloud &
DevOps
Training

Master the tools and best practices used by industry experts to design, automate, deploy, and manage scalable cloud solutions.

14+Years industry experience
700+Students trained
100%Practical training
LIVE LAB / 01 Running
CODE
BUILD
DEPLOY
SCALE
$ kubectl rollout status deployment/nxtzen-app
100%Practical
Training
AI + AIOps
Multi-cloud

Build in-demand skills.
Accelerate your career.

The modern technology engine

What are cloud
and DevOps?

01

Cloud computing

Cloud platforms provide on-demand computing, storage, databases, networking, and security without owning physical infrastructure. Teams can launch faster, scale globally, and pay for what they use.

02

DevOps

DevOps connects development and operations through culture, automation, feedback, and shared responsibility. It helps teams release software more often while improving stability and reliability.

03

Together

Cloud gives teams flexible infrastructure. DevOps turns that infrastructure into a repeatable delivery system—so ideas move from code to customers safely and quickly.

Course high-level content

One practical path.
Six essential skills.

Learn by building real workflows across cloud platforms, containers, automation, observability, and AI-assisted operations.

01

Cloud Fundamentals

AWS, Azure & GCP core services

  • Compute
  • Storage
  • Networking
  • Databases
  • Security
02

DevOps Culture

Practices, mindset & collaboration

  • Agile & Scrum
  • DevOps principles
  • Shift-left approach
  • Continuous improvement
03

Containers & Orchestration

Modern infrastructure management

  • Docker
  • Kubernetes
  • Helm charts
  • Service mesh
  • Best practices
04

CI/CD & Automation

Build, test and deploy with confidence

  • CI/CD pipelines
  • Jenkins & GitHub Actions
  • GitLab CI & Argo CD
  • Infrastructure as code
05

Monitoring & Observability

Performance, reliability & insight

  • Prometheus
  • Grafana
  • ELK stack
  • CloudWatch
  • Alerting & dashboards
06

AI in DevOps (AIOps)

Intelligent operations with AI

  • AI/ML in operations
  • Anomaly detection
  • Intelligent automation
  • Predictive insights
Animated engineering workflows

See modern delivery
systems in motion.

Visual labs make complex Cloud and DevOps concepts easier to understand—from the first commit to a secure production deployment.

01 / GIT LIVE LAB LIVE FLOW

Git workflow to release

Follow a change from feature branch to pull request, integration testing, release approval, and a controlled merge into main.

main
develop
feature
CommitPull requestReviewMerge
02 / TERRAFORM LIVE LAB EXECUTING

Provision infrastructure safely

Watch Terraform initialise providers, calculate a change plan, request approval, and create production-ready cloud resources.

nxtzen-iac / production
$ terraform init ✓ providers ready
$ terraform plan + 6 to add
$ terraform apply Apply complete!
_
VPCEKSRDSIAMS3
03 / CLOUD RESOURCES LIVE LAB PROVISIONED

Connect the cloud platform

See networking, compute, data, identity, security, and resilient services operating together as one production environment.

Cloud
Compute
Data
Security
Network
Highly availableSecureScalable
04 / MONITORING LIVE LAB OBSERVING

Detect, correlate, and recover

Observe live metrics, logs, traces, SLO health, and automated incident signals across a modern production workload.

Services healthy99.98%
Metrics Logs Traces
AI correlationLatency anomaly grouped · probable DB saturation
From DevOps to intelligent DevSecOps

Delivery evolves when
security and AI join the loop.

Start with continuous development and operations, embed security at every stage, then use AI to improve detection, decision-making, and automation.

01 · CONTINUOUS DEVOPS LOOP
DevOps infinity loop lifecycle: Plan, Code, Build, Test, Release, Deploy, Operate and Monitor

Plan, code, build, test, release, deploy, operate, and monitor as one measurable continuous delivery system.

Embed security · add AI context
02 · DEVSECOPS + AIOPS
SECUREshift left
AIOpspredict & assist
1Threat model2Code + SAST3SBOM + Scan4Security test5Signed release6Policy deploy7Runtime protect8AI detect
LLM-assisted analysis

Security controls travel with the software while AIOps correlates telemetry, explains risk, and recommends governed actions.

Containers to production orchestration

Package once.
Run reliably at scale.

Learn how an application moves from a Docker image, to a multi-container local environment with Docker Compose, and finally to a resilient Kubernetes deployment.

01

Docker

Build an immutable image from a Dockerfile, tag it, scan it, and publish it to a trusted container registry.

SourceDockerfileImageRegistry
02

Docker Compose

Describe a repeatable local stack—application, API, database, and network—in one declarative compose file.

WebAPIDBNetwork
03

Kubernetes

Use declarative APIs and controllers to schedule containers, maintain desired state, scale replicas, and roll out updates safely.

ManifestAPI serverSchedulerPods
KUBERNETES CLUSTER ARCHITECTUREControl plane, worker nodes, pods, and cloud integration
Kubernetes architecture showing the control plane with etcd, kube-api-server, scheduler, controller manager and cloud controller manager connected to two worker nodes containing kubelet, kube-proxy, pods and the container runtime, plus the cloud provider API.
The API server is the central communication point. Control-plane components reconcile desired state, while kubelets and kube-proxy manage workloads and networking on each worker node.
AIOps · LLMs · MCP

Give DevOps teams an
AI-assisted control plane.

AIOps correlates telemetry and detects anomalies. Large language models help engineers explain incidents, generate runbooks, review infrastructure code, and summarise changes. Model Context Protocol (MCP) connects an AI assistant to approved tools and operational context so it can query systems safely and take governed actions.

Incident summarisation and probable root cause Terraform, pipeline, and runbook assistance MCP-based access to Git, cloud, observability, and tickets Human approval gates for production actions
LLM Agentreason · explain · assist
MCP Servertools · context · policy
Git repositories
CI/CD tools
Kubernetes
Confluence
Observability
Cloud APIs
Human approval required
Platforms & tools

Learn the ecosystem
teams use every day.

01Cloud
AWSMicrosoft AzureGoogle Cloud
02Build & Source
GitGitHubGitLabJenkins
03Infrastructure
DockerKubernetesHelmTerraform
04Observe
PrometheusGrafanaELKCloudWatch
05Security & AIOps
SAST / DASTTrivyOPA / KyvernoLLMsMCP
AI in cloud & DevOps

Operate with
more intelligence.

AI is changing how cloud teams design systems, detect problems, automate repetitive work, and make operational decisions. The course introduces practical AIOps concepts without losing sight of engineering fundamentals.

Smarter operations

Use AI-assisted incident triage, log analysis, and root-cause suggestions to reduce recovery time.

Predictive reliability

Spot unusual infrastructure behavior and capacity risks before they become production incidents.

Faster engineering

Apply generative AI to documentation, pipeline creation, code review, and infrastructure automation.

Why learn cloud & DevOps?

Build a career
that keeps moving.

Build and deploy scalable applicationsWork with cutting-edge technologiesDevelop future-ready engineering skillsCollaborate across modern product teams