AI is no longer just about chatbots spitting out text: it’s becoming part of the software development workflow itself. From code generation to testing, deployment, and even CI/CD, new tools are reshaping how engineers build.
AI Dev Tools Zoomcamp 2025 is designed to help you make sense of this ecosystem by building with it. Over six modules, you’ll go from experimenting with AI coding assistants to creating your own agent that scaffolds real applications. The next cohort starts November 18, 2025.
In this guide, you’ll find:
- What You’ll Learn in AI Dev Tools Zoomcamp 2025
- Why This Course Matters
- Building Your Project Portfolio
- Who This Course Is For
- How It Works
What You’ll Learn in AI Dev Tools Zoomcamp 2025
The course is organized into six modules. Each builds on the last, moving from simple AI-assisted coding to building full agents and automating workflows.
Module 1: Vibe Coding and AI Tool Landscape
We begin by exploring what “vibe coding” really means in practice: writing code more fluidly by allowing AI to handle scaffolding, suggestions, and bug fixes.
Through a Snake game example, you’ll see how AI can pair with you inside the editor.
Along the way, you’ll compare the tools most developers are trying right now:
- OpenAI ChatGPT and Anthropic Claude: conversational AIs that can reason about code and answer developer questions.
- GitHub Copilot: the well-known coding assistant integrated into VS Code and JetBrains IDEs.
- Cursor: an AI-first IDE that deeply integrates assistants into the coding workflow.
- Bolt and Lovable: “project bootstrappers” that generate a working app from a simple prompt, a short description of the task for an AI tool, giving you a head start.
Module 2: Shipping an End-to-End Project
Next, you’ll apply these tools to something bigger. Using an AI coding assistant, you’ll:
- Build Snake in React and TypeScript: React is the most popular JavaScript UI library, and TypeScript is a robust, strongly typed programming language that extends JavaScript with static typing for enhanced reliability.
- Define APIs with OpenAPI: a specification for describing APIs in a machine-readable way.
- Generate a FastAPI backend: a modern Python framework for quickly building APIs.
- Set up CI/CD: continuous integration and deployment pipelines that automate testing and releases.
The goal is to explore how far you can take an idea from conception to deployment with AI as your teammate.
Module 3: Extending Assistants with the Model Context Protocol (MCP)
Here you’ll go deeper into how AI connects to the real world using Model Context Protocol (MCP), an open standard that lets assistants securely access external systems.
With MCP, AI can plug into various tools and platforms.
In this course, we’ll use it to connect to:
- GitHub: for repo triage and PR summarization.
- Databases (SQL): for running queries.
- CI/CD pipelines: for scripted edits, builds, and deployments.
You’ll practice these workflows while also learning the trade-offs between local servers (run on your machine) and remote servers (hosted in the cloud), and how to manage permissions safely.
Module 4: Building Your Own Coding Agent
Instead of just using tools, you’ll learn how to make them.
Starting with a Django template, a popular Python framework for web apps, you’ll create an AI coding agent: software that can scaffold and extend projects automatically.
You’ll also explore orchestration frameworks, libraries for managing multiple tools/agents, and see how agents differ from simple assistants.
By the end, you’ll have a Django app built and modified by your own AI agent.
Module 5: AI for Testing, CI/CD, and DevOps
Coding is only half the job: shipping and maintaining software is the other.
This module shows how AI supports testing and operations:
- Automated test generation and coverage checks in CI: catching bugs earlier.
- AI-assisted PR reviews: summarizing changes and highlighting risks.
- Release notes and changelog drafting: saving time in documentation.
- Incident postmortems and on-call copilots: helping engineers handle production issues.
Module 6: Low-Code and No-Code AI Automation (n8n)
Finally, you’ll see when it’s faster to automate instead of coding. With n8n (an open-source automation platform), you’ll build workflows that connect:
- Slack, GitHub, Jira, Notion: team communication and project management tools.
- Databases: for pulling and storing structured data.
You’ll use LLM nodes (blocks that call large language models) and webhooks (triggers for real-time automation) to ship lightweight assistants without maintaining servers. This closes the loop from coding agents to no-code automation.
Why This Course Matters
Learning a new tool can be straightforward: watch a demo, skim the documentation, and go through trial and error until you find a workflow that works. Such experimentation is a viable strategy, but it can be time-consuming, and the sheer number of tools available only adds to the challenge.
What’s even harder, and more important for an efficient developer, is understanding how these tools fit together into a pipeline, and whether they genuinely help you ship software.
That’s the focus of AI Dev Tools Zoomcamp 2025. We designed it with developer workflows in mind. By the end, you won’t just know what Copilot, Cursor, or n8n are; you’ll have used them to build complete projects and integrate them into real processes.
Building Your Project Portfolio
Every module includes a project that applies the concepts in practice.
By completing them, you’ll build a portfolio that reflects the full development lifecycle:
- Ship applications with AI support, from a simple Snake game to a deployed React + FastAPI app.
- Extend assistants with the Model Context Protocol (MCP), connecting them to systems like GitHub and databases.
- Build your own coding agent capable of scaffolding and modifying Django projects.
- Apply AI across the development lifecycle, including testing, CI/CD, DevOps, and incident response.
- Automate workflows without code using n8n to link everyday tools like Slack, GitHub, and Jira.
By the end, you’ll have both a portfolio of projects and a certificate to demonstrate practical skills in applying AI to engineering work.
Who This Course Is For
This course is designed for:
- Developers who want to explore how AI tools fit into their workflow.
- Engineers aiming to boost productivity with coding assistants, agents, and automation.
- Learners who prefer project-based practice over theory-heavy tutorials.
No prior experience with AI tools is required, but basic programming knowledge (Python, JavaScript, or similar) is recommended.
How It Works
The course is designed to be flexible, but structured enough to help you build real projects.
Homework Assignments
Each module includes homework to practice what you’ve learned and track your progress. You can also compete on the course leaderboard, which adds a fun, competitive element. However, completing homework assignments does not affect certificate eligibility.
Final Project and Certificate
To earn your free certificate, you’ll complete a final project and submit it for peer review. This ensures you demonstrate practical application of the concepts.
Online and Free
All materials are openly available, including lessons, slides, and recordings.
Community Support
You won’t be learning in isolation. The Zoomcamp is supported by DataTalks.Club community on Slack, where thousands of learners exchange ideas, ask questions, and provide feedback. This space makes it easier to stay accountable, find study partners, and connect with peers who share your interests. Many learners have turned these connections into collaborations and long-term professional relationships.
Active discussions and support in the ML Zoomcamp Slack community channel
Learning in Public
A unique feature of our Zoomcamps is our “learning in public” approach, inspired by Shawn Wang’s (@swyx) article on the topic. Instead of keeping your progress private, you’re encouraged to share assignments, reflections, and projects online.
Explaining what you’ve learned helps you understand it better, builds confidence, and creates visible proof of your skills. To encourage this, we award bonus points on the leaderboard for public posts about your work.
This practice has led to real opportunities for learners. For example, Pastor Soto, who joined ML Zoomcamp without a LinkedIn account, began posting his projects publicly. These posts not only accelerated his learning but also attracted attention from recruiters at Meta and DeepLearning.AI. Similarly, Daniel Egbo shared his progress on LinkedIn, which opened the door to collaborations on projects such as deploying models with Intel’s OpenVINO toolkit.
Ready to Start Your AI Development Journey?
AI Dev Tools Zoomcamp 2025 gives you hands-on experience with the tools that are reshaping software development. Boost your productivity, build a portfolio of AI-powered projects, and stay ahead of the curve in your career.
Join thousands of developers who are already using AI to build better software, faster.