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Kubernetes & Cloud-Native AI Learning Path

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Master AI, ML & Context Foundations
Learn by Doing - Prompt Engineering 101
5h 0m
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Detailing for Clarity
Persona Play
Delimiters
Step-by-Step Clarification
Illustrating Through Examples
Specifying Output Length
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Vector Databases
4h 26m
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Vector Database Foundations
From Data to Vectors: The Embedding Layer
Vector Similarity Explained
Building Vector Storage on AWS S3
Vector Database Landscape
Vector Database Internals
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MCP For Beginners
2h 31m
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mcp Introduction
Core Concepts
Leveraging MCP for Daily Work
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AI Agents Fundamentals
1h 33m
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LLMs work in real time
Embeddings & Vector Representations
LangChain
Vector Databases Deep Dive
Build Semantic Search Engine
Model Context Protocol
Advanced MCP Concepts
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Build with Generative AI & Developer Tools
Introduction to OpenAI
4h 10m
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Text generator
Advanced usage techniques
Content moderation
Image generation and captioning
Fine-tuning techniques
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Claude Code For Beginners
8h 32m
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Getting Started with Claude Code
Working with Claude Code
Code Review with Claude Code
Security Auditing with Claude Code
Terminal Productivity
Advanced Features
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Optional
Cursor AI
3h 48m
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Introduction to Cursor
Mastering Autocompletion in Cursor
Interacting with your Codebase
Inline Editing and Debugging in Cursor
Terminal Productivity
Customizing Cursor
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Optional
Generative AI in Practice: Advanced Insights & Operations
3h 52m
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Evolution of AI Models
Retrieval-Augmented Generation (RAG)
AI Application(LLM) on Azure
Large Language Models(LLM)
Prompting Techniques in LLM
Application and Assessment of LLMs
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Running Local LLMs With Ollama
2h 15m
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Running Your First Model
Essential Ollama CLI Commands
Ollama REST API Endpoints 
OpenAI Compatibility for Ollama
Customizing Models
Uploading Custom Models
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Serve Models & Orchestrate Workloads on K8s
Loop Engineering
0h 45m
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introduction
The Scorekeeper
Six Components of Loop Engineering
Automations
Worktrees
Connectors & Plugins
Sub-agents
Memory
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KServe Fundamentals: Serving ML Models on Kubernetes
1h 0m
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KServe Architecture
Serving a Generative Model
Serving a Predictive Model
Troubleshooting Basics
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Fundamentals of MLOps
5h 8m
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Data Collection and Preparation
Model Development and Training
Model Deployment and Serving
Automating Insurance Claim Reviews with MLflow and BentoML
Data Security and Governance
AWS SageMaker
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Host AI Agents & Drive K8s Automation
KAgent: Host Your AI Agents on Kubernetes
4h 18m
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KAgent Installation & Architecture Overview
KMCP Installation & Overview
System Prompts for AI Agent Building
Debugging AI Agents
Creating AI Agents - Bring It all together
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Introduction to K8sGPT and AI-Driven Kubernetes Engineering
1h 0m
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The AI Revolution in Kubernetes and DevOps
Introducing K8sGPT and AI Agents
The Future of DevOps Engineers
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How long will it take for me to complete?

I can spend
hours / day
2
4
6
≈ 9 Weeks
≈ 7 Weeks
≈ 4 Weeks
≈ 2-3 Months
≈ 5 Weeks
≈ 4 Weeks
≈ 3 Months
≈ 2 Months
≈ 4 Weeks
≈ 3 Weeks
≈ 2 Months
≈ 1-2 Months
* This is based on averages from our students. This may change depending on your experience and level of expertise.

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AI learning paths

Path

General AI & AI Engineering

10 Courses
106h
Path

Azure AI

10 Courses
139h
Path

Kubernetes & Cloud Native AI

13 Courses
120h
Path

AWS AI

12 Courses
127h

Role based learning paths

Path

System
Administrator

13 Courses
341h
Path

Kubernetes
Administrator

12 Courses
250h
Path

DevOps
Engineer

17 Courses
350h
Path

Site Reliability
Engineer

13 Courses
320h
View All

FAQs

What is CI/CD?

CI/CD stands for Continuous Integration and Continuous Deployment. It is a set of practices and tools that enable developers to automatically build, test, and deploy software applications in a consistent and streamlined manner.

What is the difference between Continuous Integration and Continuous Deployment?

Continuous Integration (CI) focuses on automating the process of merging code changes from multiple developers into a shared repository and running automated tests to detect integration issues. Continuous Deployment (CD) takes CI a step further by automating the deployment of successfully tested code changes to production environments.

What are the benefits of implementing CI/CD?

CI/CD brings several benefits, including increased development speed, faster time to market, improved code quality, early bug detection, reduced manual errors, and easier collaboration among development teams.

What are some popular CI/CD tools?

Some popular CI/CD tools include Jenkins, GitLab CI/CD, CircleCI, Travis CI, TeamCity, and GitHub Actions. These tools provide automation capabilities for building, testing, and deploying applications.

How does CI/CD integrate with version control systems?

CI/CD tools integrate with version control systems, such as Git, to automatically trigger build and deployment processes whenever changes are pushed to the repository. This ensures that code changes are continuously tested and deployed.

What is the role of automated testing in CI/CD?

Automated testing is a crucial component of CI/CD. It includes unit tests, integration tests, and other types of automated tests that are executed as part of the CI/CD pipeline. These tests ensure the quality and reliability of the software before it is deployed.

Can CI/CD be used with containerized applications?

Yes, CI/CD is well-suited for containerized applications. Containers provide a consistent and reproducible environment for building, testing, and deploying applications, making it easier to integrate CI/CD pipelines into container-based workflows.

What are some common challenges in implementing CI/CD?

Some common challenges include managing complex deployment pipelines, ensuring compatibility across different environments, handling database migrations, maintaining good test coverage, and managing secrets and configurations securely.

Is CI/CD applicable only to cloud-based applications?

No, CI/CD can be applied to applications deployed in various environments, including on-premises, hybrid cloud, and multi-cloud environments. The principles of CI/CD can be adapted to different deployment scenarios.

What skills are important for implementing CI/CD?

Skills in version control systems (e.g., Git), scripting and automation (e.g., Bash, Python), knowledge of CI/CD tools, containerization (e.g., Docker), and infrastructure-as-code (e.g., Terraform) are valuable for successful implementation of CI/CD.

FAQs

Why learn to run AI on Kubernetes?

As AI moves into production, teams need to serve models and host agents at scale. This path teaches distributed training,model serving, and running autonomous AI agents natively on Kubernetes — a specialized, high-value skill set at theintersection of MLOps and platform engineering.

Where should I start my learning journey?

  • Complete the shared foundations (Steps 1 & 2) so you're comfortable with models, agents and generative-AI tooling first.
  • Then move to the cluster: KServe for model serving and Fundamentals of MLOps, before hosting agents with KAgent

Do I need Kubernetes experience before starting?

A working knowledge of Kubernetes is strongly recommended — this is the most infrastructure-heavy path. If you're new to K8s, cover a core Kubernetes course first; the AI-on-K8s courses assume you can already work with clusters, pods and CRDs.

Are there any certifications in this path?

This path is skills- and project-focused rather than exam-focused. Instead of a certification exam, you finish with hands-oncapability in cloud-native model serving (KServe), MLOps, agent hosting (KAgent), and AI-driven cluster troubleshooting(K8sGPT).

What is the recommended progression?

  1. MakerAWS ML Engineer – Associate, SageMaker Deep Dive
    Prompt Engineering, Vector Databases, MCP, AI Agents
  2. Build with Generative AI & Developer ToolsOpen
    AI, Claude Code, Cursor AI, Ollama
  3. Serve Models & Orchestrate on K8s
    Loop Engineering, KServe, Fundamentals of MLOps
  4. Host AI Agents & Drive K8s Automation
    KAgent, K8sGPT

How long will it take to complete?

The full path is roughly 120 hours. From scratch: 2 hrs/day → ~9 weeks, 4 hrs/day → ~5 weeks, 6 hrs/day → ~3 weeks.With AI foundations and K8s basics in place, Steps 3 & 4 take around 3–4 weeks at 2 hrs/day.