School of Cloud & DevOps Engineering · Intermediate

Google Cloud Engineering (GCP) Course

Design and deploy cloud-native solutions on Google Cloud with strong architecture, operations, security and automation practices.

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Google Cloud Engineering (GCP) course illustration at Brightnest AI Academy

Google Cloud Foundations and IAMVPC Networking and ConnectivityCompute, Serverless and ContainersStorage, Databases and Data Services

Google CloudIAMgcloudVPCCloud DNS

Duration10–12 weeks76 hours

Batch startsConfirm with admissionsOpen for registration

Learning formatLive mentor-led instruction, guided labs, assignments, feedback and project reviewsLive online / classroom

Curriculum8 modulesLabs and assessed capstone

Portfolio2 projectsPlus module evidence

LevelIntermediateCourse level

PathwayCloud & DevOps EngineerRelated career pathway

How you will learn

Live instructor-led sessions that connect concepts to real workplace decisions.

Guided labs and workshops in every module.

Assignments, checkpoints and practical feedback.

Portfolio documentation, demonstrations and capstone review.

Access to recordings and LMS resources according to the published batch policy.

Career preparation based on completed work and target roles.

Course curriculum

What you will learn, module by module

Design cloud-native infrastructure and services on Google Cloud. Progress from Google Cloud Foundations and IAM to Google Cloud Engineering Capstone through guided labs, assessed projects, and portfolio evidence.

01Module 1 · 6 hoursGoogle Cloud Foundations and IAMCreate a governed GCP project with IAM roles, service accounts and budget controls.

Topics you will cover

Projects
Folders/organizations
Regions/zones
Billing
IAM
Service accounts
gcloud CLI

Tools and platforms: Google Cloud, IAM, gcloud
Portfolio evidence: GCP project baseline
Assessment: Foundations quiz

02Module 2 · 10 hoursVPC Networking and ConnectivityBuild a segmented VPC with private workloads and controlled internet egress.

Topics you will cover

VPCs
Subnets
Routes
Firewall rules
Cloud DNS
Load balancing
Cloud NAT

Tools and platforms: VPC, Cloud DNS, Load Balancing
Portfolio evidence: GCP network architecture
Assessment: Networking lab

03Module 3 · 10 hoursCompute, Serverless and ContainersDeploy an application using VM, Cloud Run and container patterns and benchmark operations.

Topics you will cover

Compute Engine
Managed instance groups
Cloud Run
Cloud Functions concepts
GKE
Artifact Registry
Workload selection

Tools and platforms: Compute Engine, Cloud Run, GKE concepts
Portfolio evidence: GCP deployment comparison
Assessment: Compute design review

04Module 4 · 8 hoursStorage, Databases and Data ServicesImplement a secure application data layer with storage and managed database services.

Topics you will cover

Cloud Storage
Persistent disk concepts
Cloud SQL
Spanner/Firestore/Bigtable awareness
Backup
Encryption
Lifecycle

Tools and platforms: Cloud Storage, Cloud SQL, database concepts
Portfolio evidence: GCP data architecture
Assessment: Data services lab

05Module 5 · 8 hoursArchitecture, Reliability and ScalingDesign a resilient application architecture with availability and recovery objectives.

Topics you will cover

Highly available design
Autoscaling
Managed services
Decoupling
Messaging
Caching
SLO thinking

Tools and platforms: Pub/Sub concepts, load balancing, managed services
Portfolio evidence: Google Cloud reference architecture
Assessment: Architecture case

06Module 6 · 8 hoursSecurity, Operations and ObservabilityCreate logging, alerting and secret-management controls for a sample service.

Topics you will cover

IAM hardening
KMS/Secret Manager
Logging
Monitoring
Alerting
Security posture
Organisation policies

Tools and platforms: Cloud Logging, Cloud Monitoring, Secret Manager/KMS
Portfolio evidence: GCP security/observability baseline
Assessment: Operations checkpoint

07Module 7 · 10 hoursInfrastructure as Code and Cloud AutomationProvision a multi-service environment using Terraform through a Git-based workflow.

Topics you will cover

Terraform
Deployment pipelines
Policy controls
CI/CD
Service configuration
Cost optimisation
Operational automation

Tools and platforms: Terraform, Cloud Build/GitHub Actions concepts
Portfolio evidence: Automated GCP infrastructure
Assessment: IaC lab

08Module 8 · 16 hoursGoogle Cloud Engineering CapstoneBuild a production-style cloud solution on GCP with automated provisioning and operational dashboards.

Topics you will cover

Architecture
IAM
Network
Compute
Data
Security
Observability

Tools and platforms: Google Cloud, Terraform, GitHub
Portfolio evidence: GCP deployment + architecture portfolio
Assessment: Capstone defence

Applied portfolio

Projects you will build

2 portfolio projects plus module evidence

Portfolio project 1

Cloud-Native GCP Application

Deploy app/services with private networking, managed data, observability, IAM and Terraform.

Architecture · Terraform · dashboards · cost/reliability review

Portfolio project 2

GCP Resilient Service Platform

Build scalable managed compute/container architecture with SLOs and recovery plan.

Deployment · SLOs · alerts · runbook · architecture defence

Course value

Why this course

Google Cloud capability is demonstrated by sound architecture, secure identity, resilient services, automation, observability, and operational evidence—not console screenshots.

The curriculum progresses from Google Cloud Foundations and IAM to Google Cloud Engineering Capstone, with guided labs, assessments, and two portfolio projects: Cloud-Native GCP Application and GCP Resilient Service Platform.

Course fit

Who this course is for

IT professionals and developers building Google Cloud engineering and architecture skills.

IntermediateCloud & DevOps Engineer

PrerequisitesLearners should understand basic networking and operating-system concepts. No prior Google Cloud experience is required.

Practical capabilities

What you will be able to do

Create a governed GCP project with IAM roles, service accounts and budget controls.
Build a segmented VPC with private workloads and controlled internet egress.
Deploy an application using VM, Cloud Run and container patterns and benchmark operations.
Implement a secure application data layer with storage and managed database services.
Design a resilient application architecture with availability and recovery objectives.
Provision a multi-service environment using Terraform through a Git-based workflow.
Build a production-style cloud solution on GCP with automated provisioning and operational dashboards.

Tools and platforms

Technology you will use in this course

Google CloudIAMgcloudVPCCloud DNSLoad BalancingCompute EngineCloud RunGKE conceptsCloud StorageCloud SQLdatabase conceptsPub/Sub conceptsCloud Logging

Career relevance

Cloud & DevOps Engineer

This course supports the development of skills relevant to roles such as Google Cloud Engineer, Cloud Administrator, and Cloud Architect pathway. The strongest learner outcome is a portfolio that shows the problem, implementation, testing or evaluation, documentation and a clear explanation of decisions—not a certificate alone.

Course evidence and instruction

Academy advisor

Ranjeet Kumar

Advisor, Brightnest AI Academy · Innovation & Growth Leader

A technologist and data leader with 15+ years of experience applying data, artificial intelligence and machine learning to complex problems, scalable products and business growth.

What our learners say

Learner experience

The cloud labs gave me evidence I could discuss in interviews—from networking and Linux to Docker, monitoring and deployment decisions.

Industry and technology ecosystem

Microsoft

Course FAQs

Clear answers before you enrol

Design and deploy cloud-native solutions on Google Cloud with strong architecture, operations, security and automation practices.

Is the Google Cloud Engineering course suitable for beginners?

This is an intermediate-level course. Learners should understand basic networking and operating-system concepts. No prior Google Cloud experience is required.

What will I build during the course?

You will complete guided labs in every module and build two portfolio projects: Cloud-Native GCP Application and GCP Resilient Service Platform. Deliverables include working files or code, documentation, testing or evaluation evidence, and a final presentation.

Which tools and platforms are covered?

Key tools include Google Cloud, IAM, gcloud, VPC, Cloud DNS, Load Balancing, Compute Engine, and Cloud Run. Additional platforms are introduced in relevant modules through practical tasks, and the toolset may evolve as industry practice changes.

How long does the course take?

The course includes approximately 76 guided learning hours across 8 modules, normally delivered over 10–12 weeks depending on batch intensity and learner practice time.

Which career paths can this course support?

The curriculum supports the development of skills relevant to roles such as Google Cloud Engineer, Cloud Administrator, and Cloud Architect pathway. Career outcomes depend on prior experience, project quality, interview readiness and market conditions; employment is not guaranteed.

Will I receive mentor and career support?

The course includes live instruction, lab support, assignment feedback, project reviews and career preparation covering portfolio development, resume writing, LinkedIn profile improvement, and interview guidance.

Ready to start?

Ready to start your Google Cloud Engineering (GCP) journey?

Review the full curriculum, experience a live class and confirm the right starting point before enrolling.

Book a Free Demo Class Speak with a Course Advisor: +91 73030 11395

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