AI Bootcamp (Capstone)

Duration: 10 Days

Description

The AI Bootcamp is a capstone course, designed as an intensive, project-driven program that brings together the full technical curriculum. Serving as the shared capstone for both the developer and data/ML engineering tracks, it challenges participants to design and build a substantial AI system end to end: from data and retrieval through LLM application logic, agents, evaluation, and production concerns. Working on a realistic project with guidance from experienced instructors, participants consolidate their skills, fill gaps, and leave with a portfolio-worthy build and the confidence to deliver AI systems in the real world.
Class time is heavily hands-on: project work and labs (70 percent) with focused instruction, design reviews, and discussion (30 percent).

Audience

Developers and data/ML engineers who have completed the core technical courses and want a capstone experience building a complete AI system. The shared culmination of both technical tracks — ideal preparation for owning an AI system end to end in a real engineering role.

Objectives

• Design a complete AI system from requirements to delivery
• Integrate data, retrieval, LLM application logic, and agents
• Apply evaluation, reliability, and security throughout
• Address production concerns: deployment, monitoring, and cost
• Collaborate and present technical design decisions
• Produce a portfolio-worthy end-to-end project

Prerequisites

The Bootcamp assumes completion of the core technical curriculum or equivalent experience. Participants should bring:

• Strong Python skills (see Python Foundations)
• Experience building LLM applications and/or ML systems
• Familiarity with agents, retrieval, and deployment concepts

Related AI Courses

See the full AI training roadmap and course directory for how this fits into a broader learning path.

This capstone draws on Building LLM Applications, Introduction to AI Agents, and MLOps & LLMOps — completing those courses first will help you get the most out of the project work.

Course Outline

• Module 1 – Kickoff and Project Design
  • Welcome and objectives
  • Capstone project options and scoping
  • Requirements, architecture, and planning
  • Forming teams and setting up
• Module 2 – Data and Retrieval Foundations
  • Preparing data and knowledge sources
  • Building retrieval and grounding
  • Design review and feedback
  • Project work
• Module 3 – Application and Agent Logic
  • Building core application and workflow logic
  • Adding agent capabilities and tools
  • Integrating components
  • Project work
• Module 4 – Evaluation, Reliability, and Security
  • Building evaluation and testing
  • Hardening for reliability and security
  • Cost and performance tuning
  • Project work and design review
• Module 5 – Delivery and Presentation
  • Deployment and operational readiness
  • Final integration and polish
  • Presenting design decisions and results
  • Capstone presentations and wrap-up