AI Dev Tools Zoomcamp

Quick Links

[!NOTE] The 2026 materials are currently being finalized. The repository shows the direction for the next cohort, but some videos, homework, deadlines, project requirements, and module details may change before the cohort starts on August 31, 2026.

Pre-Course Live Events

Before the 2026 cohort starts, we are running a free live event series to help you understand the updated course content and prepare for the first week.

The sessions are optional, but useful if you want to see the workflows in action before starting the course.

About the Course

AI Dev Tools Zoomcamp is a free, practical course for developers and technical data professionals who want to learn disciplined AI-assisted software development.

The course is built around one core workflow:

Give AI tools the right context, use them for the right job, review what they produce, test the result, and ship software with guardrails.

You will compare modern AI developer tools, build and deploy a full-stack application, extend coding agents with MCP and reusable capabilities, and use open-source AI tools for security, audit, and DevOps workflows.

This is not a prompt-engineering-only course, and it is not a course about training models or building RAG systems. The focus is day-to-day software development with AI coding assistants, agents, tests, CI/CD, deployment, documentation, and review.

Who Should Join

This course is for you if you:

• Already write basic code in Python, JavaScript, TypeScript, or a similar language.
• Want to use AI coding tools professionally, not only for isolated snippets.
• Want a repeatable workflow for planning, prompting, reviewing, testing, debugging, and shipping code.
• Care about maintainability, tests, deployment, documentation, and code review.
• Want to build a project that demonstrates practical AI-assisted development skills.
• Benefit from cohort deadlines, community support, peer review, and certificate eligibility.

The course is especially relevant for software engineers, ML engineers, MLOps engineers, AI engineers, data scientists, data engineers, analysts who write code, and technical students who want practical project experience.

This course is probably not the right fit if you have never programmed before, want a non-technical AI introduction, or mainly want model training, fine-tuning, LangChain, vector databases, or RAG.

Prerequisites

You do not need prior AI tools experience.

You should have:

• Basic programming ability in Python, JavaScript, TypeScript, or a similar language.
• Comfort using the command line.
• Git and GitHub basics.
• Willingness to read, review, debug, and maintain AI-generated code.

Helpful, but not required:

• Experience with a code editor such as VS Code, Cursor, or a similar IDE.
• Basic web development familiarity.
• Basic Docker familiarity.

Not required:

• Prior coding assistant or coding agent experience.
• Django experience.
• A powerful machine or GPU.

How to Take the Course

You can follow the course as part of the live cohort or use the materials self-paced.

[!IMPORTANT] "Live cohort" does not mean mandatory live classes. Lectures are pre-recorded. Live cohort means shared deadlines, scored homework, leaderboard participation, peer review, community momentum, and certificate eligibility.

Self-paced steps:

1. Follow the materials in this repository.
2. Watch the videos in the course playlist.
3. Ask questions and share progress in DataTalks.Club Slack.
4. Do the homework for practice and build a portfolio project.

Syllabus

Module 1: AI-Native Developer Workflow

Learn how to use AI developer tools without handing over control of the codebase.

You will:

• Compare chat assistants, terminal coding agents, agentic IDEs, cloud agents, and project bootstrappers.
• Learn when to use tools such as ChatGPT, Claude, Gemini, Claude Code, Codex, Gemini CLI, Cursor, GitHub Copilot, Antigravity, Lovable, Bolt, and similar systems.
• Practice context engineering with AGENTS.md, repository instructions, product specs, architecture notes, testing guidelines, and security checklists.
• Complete a small feature using a disciplined loop: spec, context, plan, edit, run, test, inspect diff, review, commit.

Outcome: a repeatable AI-assisted development workflow you can use in your own repositories.

Module 2: Build and Ship an AI-Assisted Full-Stack App

Use AI tools to build an end-to-end application while keeping the engineering workflow explicit.

You will:

• Write a product spec and acceptance criteria.
• Build a frontend prototype with AI assistance.
• Define the API contract with OpenAPI.
• Implement a FastAPI or Django backend.
• Add database support.
• Add unit and integration tests.
• Containerize the app with Docker.
• Deploy the app.
• Add CI/CD where possible.

Outcome: a deployed full-stack application with tests, Docker setup, OpenAPI contract, and reproducible development instructions.

Module 3: Coding Agent Capabilities: MCP, Skills, Plugins, and Custom Agents

Learn how modern coding agents are extended, customized, and connected to external tools.

You will:

• Understand MCP clients, servers, tools, resources, prompts, and communication modes.
• Configure MCP-enabled workflows in coding tools.
• Use reusable instructions, workflows, commands, skills, hooks, and specialized subagents.
• Explore plugin and extension patterns.
• Build a small agent extension pack around your app.
• Discuss local vs. remote tools, permissions, and security risks.

Outcome: project-specific agent capabilities that make AI tools more useful and safer inside your development workflow.

Module 4: Open-Source AI Tools for Security, Audit, and DevOps

Use open-source AI tools around the production workflow, not only during coding.

You will:

• Use AI-assisted pull request review and audit tools.
• Connect AI workflows to security scanning and policy checks.
• Explore agent, MCP, and skill security scanning.
• Use AI-assisted diagnostics for infrastructure and incidents.
• Discuss AI tool governance, secrets, data exposure, and auditability.

Example tools include PR-Agent, Semgrep MCP, Snyk Agent Scan, K8sGPT, LiteLLM, Ollama, and related open-source tooling.

Outcome: a security, audit, and DevOps hardening package for your final project.

Final Project

The final project applies the course workflow to an end-to-end application of your own.

You will:

• Choose a problem and describe the expected system behavior.
• Build a frontend.
• Build a backend.
• Define frontend/backend communication with an OpenAPI contract.
• Add database or persistent storage.
• Test the application.
• Containerize the application.
• Deploy it so others can try it.
• Document how AI tools, prompts, workflows, agent instructions, MCP, or automation were used.

For the live cohort certificate path, projects are evaluated through peer review. Learners must pass the final project and complete the required peer reviews.

Certificate

Certificates are available during the live cohort for learners who complete the final project and the required peer reviews.

Homework helps you stay on track, but the certificate is based on the final project and peer review requirements. See Certification for the general certificate process.

Self-paced learners can use all materials for free, but certificate eligibility requires participation in a live cohort.

Instructors

• Alexey Grigorev
• Bhavani Ravi
• Moein Foroughi

Learner Stories

This course fundamentally changed how I approach AI development. I moved from "building models" to designing AI-assisted systems that are faster to ship and easier to iterate on.

During the course, I built a portfolio optimization tool, a full-stack application using ChatGPT, Lovable, and Antigravity, and a structured GitHub project with reproducible documentation.

• Yann Pham-Van, Freelance Data Scientist

The course taught me how to use coding agents effectively, debug issues, and gave me exposure to MCPs, tools, and prompts. It helped me conceptualize any idea into a working prototype.

• Revathy Ramalingam, Senior Software Engineer at Yalabs Solutions

During the course I built a Finnish learning website which helps English users practice reading, writing, listening, and speaking skills for the Finnish language.

I used Antigravity with Gemini and Claude, a Context7 documentation MCP server, TypeScript, Python, Next.js, FastAPI, SQLite, and CI/CD with GitHub Actions.

• Kaiquan Mah, Data Scientist at Total eBiz Solutions

Community and Support

Slack

Join DataTalks.Club Slack and use the #course-ai-dev-tools-zoomcamp channel for course questions, troubleshooting, and discussion.

Before posting questions, please check:

• Question guidelines
• Community guidelines

Announcements

Course announcements are posted in the Telegram channel. Telegram messages are also reposted to the course Slack channel.

Learning in Public

Share your progress with the hashtag #aidevtools and tag Alexey Grigorev or DataTalksClub. Learning in public helps you stay accountable, meet other learners, and build visible proof of your work.

See the learning in public guide.

Sponsors

Interested in supporting DataTalks.Club and this course? Reach out to [email protected].

FAQ

For the full list, see the AI Dev Tools Zoomcamp FAQ.

Is this course free?

Yes. The videos, materials, homework, and live cohort participation are free.

Do I need AI experience?

No. Prior AI coding tool experience is not required. You should be able to write basic code and work with a project repository.

Do I need to be a software engineer?

No. The course is also relevant for data scientists, ML engineers, MLOps engineers, data engineers, analysts who write code, and technical students. You do need basic programming ability.

Are there live classes?

Lectures are pre-recorded. The live cohort adds deadlines, scored homework, a leaderboard, peer review, community support, and certificate eligibility.

Can I take the course self-paced?

Yes. You can use the videos and repository materials anytime. Self-paced learners are not certificate-eligible unless they join a live cohort.

Do I need a powerful computer or GPU?

No. A powerful machine or GPU is not required.

Is this course about RAG or model training?

No. The course focuses on AI-assisted software development workflows: coding assistants, agents, MCP, testing, CI/CD, deployment, documentation, review, security, audit, and DevOps.

About DataTalks.Club

Most DataTalks.Club activity happens in Slack. We post updates there, discuss course questions, and help learners support each other.

At DataTalks.Club, we organize online events, community activities, and free courses. Learn more in the DataTalks.Club docs.