AI-Assisted Platform Engineer GCP Edition – Live Training Demo

About the Instructor:

Gopi is a seasoned Cloud & DevOps Engineer with over 10 years of experience in designing, automating, and managing modern cloud infrastructure. His expertise spans Platform Engineering, cloud automation, and production-grade DevOps practices, helping organizations build secure and scalable cloud environments. Throughout his career, he has worked extensively with Google Cloud Platform (GCP), Kubernetes (GKE), Terraform, Docker, Jenkins, and CI/CD, delivering enterprise-level infrastructure automation solutions. He specializes in Infrastructure as Code (IaC), cloud-native deployments, and building reliable platforms for modern applications. As an instructor, Gopi has successfully trained 200+ students through live, hands-on learning. His teaching approach emphasizes practical implementation, real-time projects, and production troubleshooting, enabling learners to gain the confidence and skills required for real-world DevOps and Platform Engineering roles. His sessions are 100% hands-on, featuring live labs, cloud deployments, Kubernetes projects, CI/CD pipeline implementation, monitoring with Prometheus and Grafana, and AI-assisted platform engineering workflows. Every concept is taught with an industry-focused, project-driven methodology to prepare students for today’s cloud engineering careers.

Live Sessions  Price:

For LIVE sessions – Offer price after discount is 350 USD 250 USD 189 USD Or 35000 INR 25000 INR 14900 Rupees

Enroll For Free Demo

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Free Demo Session:

22nd September @ 8:00 PM – 9:00 PM (IST) (Indian Timings)

22nd September @ 10:30 AM – 11:30 PM (EST) (U.S Timings)

22nd September @ 3:30 PM – 4:30 PM (BST) (UK Timings)

Class Schedule:

For Participants in India: Monday to Friday @ 8:00 PM – 9:00 PM (IST)

For Participants in the US: Monday to Friday @ 10:30 AM – 11:30 PM (EST)

For Participants in the UK: Monday to Friday @ 3:30 PM – 4:30 PM (BST)

What students have to say about Gopi:

👨 Rahul Verma (System Administrator) – The way the course builds from Linux and Git fundamentals up to GCP and Kubernetes made everything click. The Platform and AI-assisted modules were the highlight for me. 👩 Sneha Reddy (QA Engineer) – Coming from a QA background, the Terraform and GCP sections took time to click, but the hands-on labs made the concepts stick. Gopi explains everything with real project context. 🙂 Aditya Kulkarni (DevOps Engineer) – The AI-assisted Platform module was genuinely useful — seeing how AI tooling fits into vulnerability triage and secure coding gave me a real edge in interviews. 👩 Priya Nair (Network Engineer) – I switched from a networking role and was worried about keeping up, but the daily hands-on labs on Kubernetes and Jenkins pipelines built my confidence step by step. 🙂 Karthik Subramanian (Cloud Support Engineer) –The live sessions on Prometheus and Grafana monitoring were exactly what I needed for my current job. Doubt-clearing over WhatsApp between classes was a big help too. 🙂Ananya Iyer (Software Engineer) Solid, structured curriculum — Terraform and GCP concepts were explained with real infrastructure examples instead of just slides. The certificate helped me negotiate a better offer.

Salient Features:

• 60 Hours of Live Training along with recorded videos
• 1 Year Access to Session Recordings
• Course Completion Certificate

Who Can Enroll for This Course?

1. Freshers & Graduates looking to start a career in Cloud and DevOps.
2. System Administrators transitioning into modern DevOps and Platform roles.
3. Software Developers who want to learn cloud infrastructure and Kubernetes.
4. DevOps Engineers aiming to strengthen CI/CD, Terraform, and automation skills.
5. Cloud Engineers specializing in Google Cloud Platform (GCP).
6. Site Reliability Engineers (SREs) wanting advanced reliability and observability expertise.
7. QA & Security Engineers moving into DevSecOps and AI-assisted security engineering.
8. IT Support & Operations Professionals upgrading to cloud-native infrastructure roles.
9. Career Switchers interested in becoming Platform or Cloud Engineers.
10. Anyone with a Laptop & Internet Connection—no prior Cloud or DevOps experience is required, as Linux and Git are taught from scratch.

 

What will I learn by the end of this course?

• Build and manage Linux-based cloud infrastructure with confidence.
• Use Git & GitHub for version control and collaborative development.
• Create secure cloud environments on Google Cloud Platform (GCP).
• Provision infrastructure using Terraform (Infrastructure as Code).
• Containerize applications with Docker and optimize images.
• Deploy and manage applications on Kubernetes (GKE) using Helm.
• Build automated CI/CD pipelines with Jenkins and GitHub workflows.
• Implement monitoring and observability usingPrometheus and Grafana.
• Apply GitOps practices with Argo CD for automated deployments.
• Use AI-assisted techniques for cloud automation, troubleshooting, security, and Platform Engineering while completing a real-world capstone project.

CHAPTER 1: Linux

1.1 Linux Fundamentals & File Management

• Linux fundamentals
• File and directory management
• File viewing and text processing

1.2 Text Processing

• grep
• Regular expressions
• sed
• Pipes and redirection
• Command chaining

1.3 System Administration

• Users and groups
• File permissions
• Processes and system management

1.4 Storage & Networking

• Disk and filesystem management
• Networking fundamentals

1.5 Remote Access & Services

• SSH and remote access
• Services and systemd
• Logs

1.6 Scripting & Troubleshooting

• Basic shell scripting
• Linux troubleshooting

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CHAPTER 2: Git & GitHub

2.1 Git Fundamentals

• Repository
• Working tree and staging
• Basic Git commands

2.2 Collaboration

• Branching
• Merging
• Conflict resolution
• GitHub repositories
• Pull requests

2.3 Git & CI/CD

• Git workflow for CI/CD
• Basic Git troubleshooting

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CHAPTER 3: Google Cloud Platform (GCP)

3.1 Cloud & Resource Fundamentals

• Cloud computing fundamentals
• Google Cloud fundamentals
• Resource hierarchy

3.2 IAM & Identity

• Identity and Access Management (IAM)
• Service accounts
• Least-privilege access

3.3 GCP Networking

• VPC networking
• Subnets
• Routes
• Firewall rules
• Cloud NAT
• DNS fundamentals
• Load balancing fundamentals

3.4 Compute & Storage

• Compute Engine
• Persistent Disk
• Cloud Storage buckets
• Storage classes
• Versioning and lifecycle management

3.5 Databases & Observability

• Cloud SQL
• Cloud Monitoring
• Cloud Logging

3.6 Reliability & GCP Project

• High availability
• GCP reliability
• GCP architecture project

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CHAPTER 4: Terraform

4.1 Infrastructure as Code Fundamentals

• Infrastructure as Code (IaC)
• Terraform fundamentals
• Terraform configuration
• Core Terraform blocks

4.2 Providers & Data

• Providers
• Resources
• Data sources
• Variables
• Outputs
• Locals

4.3 Logic & Expressions

• Expressions
• Functions
• Collections
• count and for_each
• Conditional expressions
• for expressions

4.4 State & Modules

• Dependencies and lifecycle
• Terraform state
• Remote state
• Terraform modules

4.5 Terraform Workflow & Project

• Terraform workflow
• Terraform best practices
• GCP infrastructure provisioning project

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CHAPTER 5: Maven

5.1 Maven Fundamentals

• Maven fundamentals
• Project structure
• pom.xml

5.2 Build Lifecycle

• Dependencies
• Maven lifecycle
• Build
• Test
• Package

5.3 Maven & CI/CD

• Maven integration with CI/CD

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CHAPTER 6: Docker

6.1 Container Fundamentals

• Container fundamentals
• Docker architecture
• Docker images and containers

6.2 Docker CLI & Dockerfile

• Docker CLI
• Image lifecycle
• Dockerfile
• Build context
• .dockerignore

6.3 Advanced Docker Builds

• Multi-stage Docker builds
• Docker networking

6.4 Storage & Registries

• Docker volumes
• Persistent data
• Container registries

6.5 Docker Troubleshooting

• Container troubleshooting

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CHAPTER 7: Kubernetes

7.1 Kubernetes Fundamentals

• Kubernetes architecture
• Pods

7.2 Kubernetes Workloads

• Deployments
• ReplicaSets
• Services
• Service discovery
• Ingress
• Ingress controllers

7.3 Configuration & Deployment Strategies

• ConfigMaps
• Secrets
• Deployment strategies

7.4 Resource Management

• Resource requests and limits
• Quality of Service (QoS)
• Health checks and probes
• Horizontal Pod Autoscaling (HPA)

7.5 Storage & Scheduling

• Kubernetes storage
• Scheduling
• Node selection
• Node affinity
• Pod anti-affinity
• Topology spread constraints
• Taints and tolerations

7.6 Security & Packaging

• Service accounts
• RBAC
• Helm

7.7 Troubleshooting & Production Project

• Kubernetes troubleshooting
• Production Kubernetes deployment project

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CHAPTER 8: Jenkins

8.1 CI/CD Fundamentals

• Jenkins fundamentals
• Jenkins architecture
• CI/CD fundamentals

8.2 Jenkins Pipelines

• Jenkins pipelines
• Jenkinsfile
• Declarative pipelines
• Credentials
• Environment variables

8.3 Jenkins Integrations

• Maven integration
• Docker build and image push
• Kubernetes integration

8.4 Deployment & CI/CD Project

• Deploying applications to Kubernetes
• CI/CD pipeline project

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CHAPTER 9: Prometheus

9.1 Monitoring Fundamentals

• Monitoring fundamentals
• Observability fundamentals
• Prometheus architecture

9.2 Metrics & Service Discovery

• Metrics
• Metric types
• Targets
• Service discovery
• Exporters
• Node Exporter

9.3 Kubernetes Monitoring

• Kubernetes monitoring
• kube-state-metrics

9.4 PromQL & Alerting

• PromQL fundamentals
• Application metrics
• Infrastructure metrics
• Alerting fundamentals

9.5 Prometheus Project

• Prometheus monitoring project

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CHAPTER 10: Grafana

10.1 Grafana Fundamentals

• Grafana fundamentals
• Prometheus integration
• Data sources
• Queries

10.2 Dashboards & Panels

• Dashboards
• Panels
• Variables
• Dashboard design

10.3 Monitoring Dashboards

• Infrastructure monitoring dashboards
• Kubernetes monitoring dashboards
• Application monitoring dashboards

10.4 Alerting & Observability Project

• Grafana alerting
• Production observability project

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CHAPTER 11: Platform Engineering & GitOps

11.1 Platform Engineering Foundations

• Introduction to Platform Engineering
• Platform Engineering vs DevOps and SRE
• Platform team responsibilities
• Platform architecture

11.2 Kubernetes as a Platform

• Kubernetes as a platform
• Infrastructure automation

11.3 GitOps Fundamentals

• GitOps principles
• Argo CD architecture
• Argo CD applications and projects

11.4 Argo CD & Deployment Automation

• Sync
• Auto-sync
• Self-healing
• Rollbacks
• Deployment automation

11.5 GitOps Repository & Environments

• GitOps repository design
• Multi-environment deployment

11.6 Platform Reliability & Developer Experience

• Platform observability and reliability
• Developer experience concepts

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CHAPTER 12: Introduction to MLOps

12.1 MLOps Fundamentals

• Introduction to MLOps
• MLOps fundamentals
• ML lifecycle and architecture

12.2 ML Lifecycle & Versioning

• Data lifecycle
• Model lifecycle
• Experiment lifecycle
• Model training
• Model versioning

12.3 Model Packaging & Deployment

• Model packaging
• Model deployment

12.4 Model Monitoring

• Model monitoring

12.5 ML Platform Infrastructure

• ML platform infrastructure
• MLOps workloads on GCP
• Containers
• Kubernetes

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CHAPTER 13: AI-Assisted Platform Engineering

13.1 AI Fundamentals for Platform Engineers

• AI fundamentals for Platform Engineers

13.2 AI-Assisted Infrastructure Automation

• AI-assisted Terraform and infrastructure automation
• AI-assisted Kubernetes troubleshooting
• AI-assisted Helm and configuration

13.3 AI-Assisted CI/CD & GitOps

• AI-assisted CI/CD
• AI-assisted GitOps

13.4 AI-Assisted Platform Monitoring

• AI-assisted platform monitoring
• Capacity analysis

13.5 AI-Assisted Developer Support

• AI-assisted developer support
• Building a small AI-powered Platform Assistant

13.6 Responsible AI

• Responsible AI
• Human validation