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AI, DevOps, Cloud & Software Engineering Tutorials

Learn Artificial Intelligence, Machine Learning, Python, Java, Git, GitHub, Docker, Terraform, DevOps, Cloud Computing, SRE, CI/CD, Spring Boot, Prometheus and Grafana through practical tutorials and complete learning paths.

AI Artificial Intelligence
DevOps Modern Engineering
Cloud Infrastructure & Automation
SRE Reliability & Observability

Learn Modern Technology Skills Step by Step

The technology industry is changing rapidly. Artificial Intelligence, cloud computing, DevOps, automation and modern software engineering are becoming increasingly connected.

This learning hub brings together practical tutorials covering AI engineering, Machine Learning, Python, Java, Git and GitHub, microservices, CI/CD, SRE, Linux, Docker, Terraform, Spring AI, Prometheus and Grafana.

Use the tutorials below to build your fundamentals, understand modern engineering practices and create practical projects that can strengthen your technology portfolio.

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AI & Machine Learning Tutorials

Explore Artificial Intelligence, Machine Learning, AI engineering, prompt engineering, AI applications, agentic AI and practical projects.

01

What Is NumPy & Why Is It Essential for AI/ML?

Learn NumPy fundamentals and understand why this important Python library is widely used in numerical computing, data science, Artificial Intelligence and Machine Learning.

▶ Watch on YouTube
02

The AI Engineer Roadmap Nobody Told You

Discover a structured AI Engineer learning roadmap for 2026 and understand how programming, Machine Learning, AI systems and modern AI tools fit together.

▶ Watch on YouTube
03

AI Job-Ready in Just 12 Weeks

Explore a practical AI career roadmap covering the skills, learning sequence and project mindset needed to prepare for modern AI opportunities.

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04

Can Spring Boot Build Agentic AI?

Explore Spring AI, Qwen and tool calling and see how Java and Spring Boot can be used to build modern agentic AI applications.

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05

AI Can Automate Every SDLC Phase

Learn how AI can support different stages of the Software Development Life Cycle, from development and testing to deployment and engineering workflows.

▶ Watch on YouTube
06

AI Product Recommendation System

Explore an Amazon-style AI product recommendation system using Gemini AI and a practical project-based approach.

▶ Watch Project
07

End-to-End Email Spam Classifier Project

Learn how an email spam classifier can be trained, built and deployed using Python and Machine Learning concepts.

▶ Watch Project
08

Build & Deploy a Gemini AI App with Colab & Gradio

Follow a practical workflow for building and deploying a Gemini AI application using Google Colab and Gradio.

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09

AI Prompt Types for Better Results

Explore useful AI prompting approaches and learn how different prompt strategies can improve interactions with AI systems.

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10

Top 5 AI Ethics Issues You Need to Know

Understand important AI ethics topics including responsible AI, bias, privacy, safety and the broader impact of AI systems.

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11

The Most In-Demand AI Role: Forward Deployed Engineer

Learn about the Forward Deployed Engineer role, responsibilities, required skills and its connection to modern AI products.

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Software Engineering & Architecture

Learn essential software engineering concepts including version control, GitHub, microservices and modern application architecture.

12

Git & GitHub for Beginners | Version Control Explained

Learn Git and GitHub fundamentals, repositories, version control, collaboration and essential workflows used by modern developers.

▶ Watch Tutorial
13

Microservices Explained: Why Modern Apps Use Them

Understand microservices architecture, monolithic applications, scalability and distributed application design.

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DevOps, Cloud & Automation Tutorials

Build practical DevOps knowledge with tutorials covering CI/CD, Linux, databases, cloud migration, Ansible and infrastructure automation.

14

CI/CD Explained: How One Command Reaches Millions

Understand Continuous Integration and Continuous Deployment and how automated pipelines help teams deliver software faster.

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15

Why Linux Basics Won't Get You Hired

Explore enterprise Linux and infrastructure skills that go beyond basic commands and are useful for modern engineering roles.

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16

Liquibase Tutorial for Beginners

Learn database version control and understand how Liquibase can help teams manage database changes across development environments.

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17

Replatform Explained: Cloud Migration Strategy

Understand replatforming and the differences between lift, tinker and shift approaches to cloud migration.

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18

Learn AWX / Ansible Tower in 20 Minutes

Get started with AWX and Ansible Tower concepts and learn how automation controllers can simplify infrastructure automation.

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SRE, Monitoring, Prometheus & Grafana

Learn Site Reliability Engineering fundamentals, service-level objectives, error budgets, monitoring, metrics and dashboards.

19

SRE Tutorial for Beginners: SLI, SLO & Error Budgets

Learn the fundamentals of Site Reliability Engineering, including SLIs, SLOs, error budgets, Prometheus and Grafana.

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20

Easy Grafana Installation on Windows

Follow a beginner-friendly step-by-step tutorial to install Grafana on Windows and start building monitoring dashboards.

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21

How to Install Prometheus on Windows

Learn how to install Prometheus on Windows and get started with metrics collection and infrastructure monitoring.

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🧭 AI & Software Engineering Learning Roadmap

Don't try to learn every technology simultaneously. A structured sequence can make your learning journey easier to manage.

01. Programming Fundamentals Start with Python or Java and learn variables, functions, OOP, data structures and problem solving.
02. Git & GitHub Learn repositories, branches, commits, pull requests and collaborative development.
03. Linux & Networking Build practical knowledge of Linux, processes, networking, HTTP and command-line tools.
04. Databases Learn SQL, database design, transactions and database version management.
05. Backend & APIs Understand REST APIs, authentication, application architecture and backend development.
06. Docker & Containers Learn containerization and how applications are packaged consistently across environments.
07. CI/CD & DevOps Learn automated testing, continuous integration and continuous deployment pipelines.
08. Cloud & Terraform Learn cloud infrastructure, Infrastructure as Code and infrastructure automation.
09. Monitoring & SRE Learn metrics, monitoring, SLIs, SLOs, error budgets, Prometheus and Grafana.
10. AI & Machine Learning Progress into Machine Learning, LLMs, AI applications, agentic AI and practical projects.
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Technology Skills Covered

Use this page as a technology learning map and choose the areas most relevant to your career goals.

Artificial Intelligence Machine Learning AI Engineering Agentic AI LLMs Prompt Engineering Python NumPy Java Spring Boot Spring AI Git GitHub Microservices Linux Docker Terraform DevOps CI/CD Cloud Computing SRE Prometheus Grafana Ansible AWX Liquibase Observability Database Version Control
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Complete Programming & DevOps Courses

If you prefer learning through a structured playlist, start with one of these complete courses and progress from fundamentals to practical development.

🐍 Python Full Course

Build your Python programming foundation and prepare for software development, automation, data science and AI.

View Python Playlist →

☕ Java Full Course

Learn Java fundamentals and build knowledge useful for backend and enterprise software development.

View Java Playlist →

🐳 Docker Full Course

Learn containerization and Docker concepts for modern application development and DevOps.

View Docker Playlist →

🚀 Ready to Build Your AI & Technology Career?

The modern technology industry is changing rapidly. Artificial Intelligence, Generative AI, Cloud Computing, DevOps, Software Engineering and Automation are creating new opportunities for developers and technology professionals.

Learn the fundamentals, build real-world projects, understand enterprise technologies and develop the practical skills needed to become job-ready in today's technology industry.

Learn the Fundamentals → Build Projects → Master Tools → Understand Enterprise Technology → Become Job-Ready

🧭 Your Modern Technology Learning Path

Build your skills step by step instead of trying to learn everything at once. Focus on fundamentals first and then progress toward advanced AI, cloud and enterprise technologies.

1
Programming Python, Java, Git and software development fundamentals.
2
Cloud & DevOps Linux, Docker, CI/CD, Terraform and cloud automation.
3
AI & Machine Learning Python AI, NumPy, Generative AI, LLMs and AI applications.
4
Enterprise Skills SRE, microservices, automation, observability and scalable systems.

❓ Frequently Asked Questions

What should I learn first to start a career in AI?

Start with programming fundamentals, especially Python, followed by mathematics and data fundamentals. Then learn NumPy, machine learning, Generative AI, large language models and practical AI application development.

Is Python important for Artificial Intelligence and Machine Learning?

Yes. Python is widely used for AI and machine learning because of its large ecosystem of libraries and frameworks, including NumPy, pandas, scikit-learn, PyTorch and many Generative AI tools.

Should developers learn DevOps and Cloud Computing?

Cloud and DevOps skills can be valuable for developers who want to build, deploy and operate modern applications. Docker, CI/CD, infrastructure as code, monitoring and cloud platforms are common technologies in modern software environments.

What technologies should a modern software engineer learn?

A strong learning path can include programming, Git and GitHub, Linux, databases, APIs, cloud computing, Docker, CI/CD, infrastructure as code, observability, microservices and Artificial Intelligence.

Where can I learn AI, DevOps and modern technology skills?

Eduarn provides technology learning resources and training covering Artificial Intelligence, software development, cloud, DevOps and other modern enterprise technology skills.

Eduarn — Learn AI, Software Development, Cloud, DevOps and Modern Technology Skills.

Learn continuously. Build practically. Grow professionally. 🚀

1 comment:

  1. Demonstrate a logical progression from programming
    fundamentals → DevOps/Cloud → AI → Enterprise Skills.

    ReplyDelete