Kubernetes Pro+ Training Program: Three Specialized Tracks
The Advanced Kubernetes Pro+ Training Program: Three Specialized Tracks is a cutting-edge course designed to bridge the gap between DevOps, MLOps, and modern AI-powered infrastructure management. This program is ideal for engineers who already understand Kubernetes fundamentals and are now ready to implement intelligent tooling like K8sGPT, kubectl-ai, and Kubeflow to streamline operations, accelerate troubleshooting, and deploy real-world machine learning workflows on Kubernetes.
Program Overview
This Advanced Kubernetes with AI & ML course is built for DevOps engineers, ML engineers, and cloud professionals who want to modernize Kubernetes operations using AI copilots and deploy machine learning workflows on Kubernetes infrastructure. Covering cutting-edge tools like K8sGPT, Kubectl AI, Kube-copilot, and Kubeflow, this program bridges the gap between infrastructure automation and intelligent platform engineering. By the end, learners will be able to troubleshoot clusters, enhance productivity using AI copilots, and deploy real ML pipelines on Kubernetes.
Key Features of DevOps Master Program
- Deep dive into AI-driven Kubernetes operations
- Hands-on training with K8sGPT, kubectl-ai, kube-copilot, and more
- Real-world Kubeflow-based MLOps workflows
- Use of LLMs to troubleshoot and optimize cluster performance
- Tools like kgateway and kubeai for context-aware K8s management
- Live instructor-led sessions with recordings and project support
Program Curriculum
Track 1: Kubernetes Intermediate Training Program
Curriculum Topics:
- Overview of Containers
- Docker Core Concepts
- Docker Image Management
- Kubernetes Core Concepts and Networking
- Kubernetes Services and Scheduling
- Kubernetes Controllers
- Kubectl Usage
- Persistent Storage in Kubernetes
- Securing the Cluster
- Logging and Monitoring the Cluster
Track 2: Advanced Kubernetes with AI & ML
Curriculum Topics:
- Quick Recap of Kubernetes Fundamentals
- K8sgpt
- Kubectl-ai
- Kube-copilot
- Kubeai
- kgateway
- Kubeflow (MLOps with Kubernetes)
Track 3: Advanced Kubernetes with Tooling
Curriculum Topics:
- Quick Recap of Kubernetes Fundamentals
- Multi-Stage Build
- Image Security
- Sidecar Pattern
- ArgoCD with Kubernetes
- Helm Charts
- Istio Service Mesh
- Kubernetes Federation
- Kubernetes on Cloud
- K3s (Light-weight Kubernetes)
- Observability in Kubernetes
Kubernetes Intermediate ( Track 1 )
Program Fee
₹ 6,000/- ₹9,000/-
Advanced Kubernetes with AI and ML ( Track 2 )
Program Fee
₹ 9,000/- ₹14,999/-
Advanced Kubernetes with Tooling ( Track 3 )
Program Fee
₹ 9,999/- ₹14,999/-
This Course Includes
- Eligibility: More than 2 years of experience
- 1 Months of Live Training
- 2 Capstone Projects, Each track
- Interview Preparation
- Resume Building
- Project Explanation
- No recorded Sessions
- Support via Email
Program Highlights
- Learn next-gen Kubernetes tools driven by AI/LLM
- Deploy and manage ML pipelines with Kubeflow
- Improve cluster management using K8sGPT & kube-copilot
- Practical use of AI copilots for DevOps automation
- Cross-functional skills for MLOps, Platform Engineering, and DevOps
Skills You'll Acquire:
GPT-based troubleshooting with K8sGPT
Command-line acceleration with kubectl-ai
Contextual K8s automation using kube-copilot and kubeai
Managing multi-cluster workloads via kgateway
Building and deploying ML workflows using Kubeflow
YAML automation, cluster tuning, LLM-enhanced DevOps skills
Tools You'll Learn:
Our Expert Trainers
- Kubernetes specialists with AI/ML background
- Experience deploying LLMs in containers
Who May Apply to this program?
- DevOps engineers, ML engineers
- Anyone curious about GenAI in production infrastructure
Project Work
- Use Kubeflow to manage ML pipelines
- Integrate K8sGPT, Kubectl AI for observability and response
- Deploy LLM apps on K8s using KGATEWAY or Helm
Industry Trends
Annual Salary
₹10–16 LPA (avg.)
Companies Hiring
Google, NVIDIA, Hugging Face, OpenAI partners
Demand
Explosive demand for GenAI-ready infrastructure
Market Growth
35% CAGR in GenAI + MLops segments
Market value
Kubeflow & AI Ops market hitting $5B+ by 2028
Job Growth
30% YoY in AI/ML + infra roles
FAQ'S
Basic knowledge of Kubernetes is expected. Prior experience with Docker and command-line tools will help you follow along easily.
Yes, this course is fully hands-on and includes practical labs using kubectl-ai, k8sgpt, and other AI copilots directly in a cluster setup.
Kubeflow is used for managing machine learning workflows on Kubernetes. You will deploy real ML pipelines using Kubeflow components such as KFServing.
Yes. You’ll build and deploy ML models using Kubeflow and learn how to automate and monitor the entire pipeline.
No. The course introduces ML pipeline deployment basics from a DevOps perspective—suitable even if you’re new to ML.
Absolutely. You’ll gain end-to-end exposure to tools and workflows currently used in MLOps and GenAI engineering roles.
Yes. You’ll connect LLMs like OpenAI or local models to tools like k8sgpt and kubectl-ai in real Kubernetes environments.
You’ll have access to session recordings, Slack/email support, and post-training guidance for project deployment.
This course is focused entirely on job-readiness. It trains you to work with production-grade AI-enhanced Kubernetes workflows, not just pass a certification.
We do not offer formal certificates. The focus is on delivering practical skills and portfolio-worthy project outcomes.
Yes. You’ll use tools like k8sgpt and kube-copilot to analyze logs, monitor issues, and troubleshoot clusters with AI assistance.
You’ll build a complete MLOps pipeline with Kubeflow on Kubernetes, integrating AI copilots for DevOps tasks.
No. We provide guidance for running local clusters using Minikube or KIND, and optional cloud deployment if desired.
Yes. This course is tailored for DevOps engineers who want to future-proof their skills with GenAI and MLOps integrations.
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