CF

Best of Live Q&A

3h 10m 42s
English
Paid

Best of Live Q&A is a 7-lesson 3 hours 10 minutes self-paced course by Zen van Riel. Best of Live Q&A gives you clear answers to real questions from live AI product sessions.

Course facts

Lessons
7
Duration
3 hours 10 minutes
Level
All levels
Language
English
Updated
Instructor
Zen van Riel
Price
Premium

Best of Live Q&A gives you clear answers to real questions from live AI product sessions. You see how engineers solve problems, avoid common traps, and ship working tools.

What You Learn

You learn how real teams move from ideas to stable AI systems. Each topic comes from work done in production.

Build Real Products

  • Move from hype to working AI features.
  • Grow a no-code MVP into a full product.
  • Set up simple and safe release steps.

Automate Your Workflow

  • Use GitHub Actions for checks and deploys.
  • Build basic CI/CD flows.
  • Create small tools for scraping and data tasks.

Set Up Your Infra

  • Run apps with Docker.
  • Host and manage your own AI models.

Build Better AI Systems

  • Move past “vibe coding” with simple tests.
  • Test AI‑native apps with clear steps.
  • Check and improve how AI agents behave.

Each lesson comes from real work. You see what breaks, what scales, and what you need to fix before you ship.

Who teaches Best of Live Q&A? Zen van Riel

Zen van Riel thumbnail

I am focused on creating AI systems that truly work in production, rather than remaining at the demo level.

As a software engineer, I work on scaling real AI solutions in production at GitHub, and I also teach developers how to adapt to the future by practically implementing artificial intelligence.

Besides my main work, I am developing AI Native Engineer — my YouTube channel and community, where I share applied AI development skills.

My experience:

  • Over 5 years of developing and implementing cloud solutions
  • Deep expertise in LLMs, cloud architecture, and full-stack AI systems
  • Experience in creating and scaling AI products

I am convinced that the best engineers use AI not only as a tool to accelerate development but also as a foundation for creating new features and AI-native platforms that solve real problems.

What lessons are included in Best of Live Q&A?

This is a demo lesson (10:00 remaining)

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#1: 001 Building Real Solutions Over Chasing AI Hype
All Course Lessons (7)
#Lesson TitleDurationAccess
1
001 Building Real Solutions Over Chasing AI Hype Demo
08:47
2
002 From no code MVPs to real projects
12:53
3
003 CI CD Automating a Scraper with GitHub Actions
29:27
4
004 Docker. The Road To a Self Hosted AI Home Lab Best of Live QA
53:11
5
005 Overcoming the Limits of Vibe Coding
32:06
6
006 How To Test AI Native Software
38:48
7
007 AI Agent Evaluation Fundamentals
15:30
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Frequently asked questions

What prerequisites are needed for this course?
This course does not explicitly list prerequisites, but a foundational understanding of AI concepts and familiarity with software development practices would be beneficial. The lessons cover advanced topics like CI/CD automation, Docker, and AI agent evaluation, suggesting some prior knowledge in these areas would help students fully engage with the material.
What projects or skills will I develop in this course?
Students will learn to transition from no-code MVPs to fully developed products, automate processes using GitHub Actions, and build self-hosted AI solutions using Docker. Additionally, they will gain skills in testing AI-native software and evaluating AI agents, which are essential for developing and deploying AI products effectively.
Who is the target audience for this course?
The course is designed for individuals involved in AI product development, particularly those looking to move from concept to working solutions. It is suitable for developers, project managers, and AI enthusiasts who are interested in understanding practical applications and overcoming common pitfalls in AI projects.
How does this course compare in depth to similar courses?
This course focuses on practical experiences and common mistakes in AI product development. Unlike courses that might concentrate on theoretical AI concepts, it emphasizes real-world applications, including automation, infrastructure building, and performance evaluation, making it suitable for those looking to apply AI in production environments.
What specific tools and platforms are covered in this course?
The course discusses the use of Docker for building self-hosted AI solutions and GitHub Actions for automating processes like CI/CD and scraping. These tools are essential for developing robust AI applications and managing the infrastructure needed for their deployment.
What topics are not covered in this course?
The course does not cover introductory AI concepts or basic programming skills. It assumes a certain level of familiarity with AI and development, focusing instead on advancing existing skills toward practical application in AI product development and deployment.
How can the skills learned in this course benefit future career opportunities?
The skills acquired in this course, such as automating workflows, deploying AI applications using Docker, and evaluating AI agents, are highly applicable in careers involving AI development and deployment. These competencies are valuable for roles in software engineering, AI product management, and technical leadership, enhancing one's ability to deliver effective AI solutions in industry settings.