Course Overview

This intensive 5-day sprint transforms your AI concept into a working prototype using proven agile methodologies adapted for AI development. Unlike traditional development sprints, this program addresses the unique challenges of AI projects including data uncertainty, model experimentation, and probabilistic outcomes. Your cross-functional team will experience the entire AI development lifecycle compressed into one week, from data exploration through stakeholder validation.

The sprint combines hands-on technical work with agile practices, ensuring rapid progress while maintaining quality. Each day builds upon the previous, with regular demos, feedback loops, and course corrections. You’ll learn to navigate the iterative nature of AI development, manage stakeholder expectations, and validate solutions with real data. By week’s end, you’ll have not just a working prototype but also a clear roadmap to production, complete with resource estimates and technical requirements.

Learning Objectives

  • Apply agile sprint methodology specifically adapted for AI development
  • Build a working AI prototype from scratch in a time-boxed setting
  • Navigate unique challenges of iterative AI development
  • Validate AI solutions with real data and stakeholder feedback
  • Create realistic production roadmaps from proof-of-concept results
  • Master rapid prototyping techniques for data pipelines and models
  • Establish effective team dynamics for AI projects

Topics Covered

  1. AI Sprint Methodology - Adapting agile for uncertainty and experimentation
  2. Data Discovery & Preparation - Rapid assessment and pipeline development
  3. Baseline Development - Building initial models with quick iteration cycles
  4. Model Refinement - Feature engineering and performance optimization
  5. Integration Development - Creating end-to-end working prototypes
  6. Stakeholder Validation - Real-world testing and feedback incorporation
  7. Production Planning - From prototype to scalable solution
  8. Team Dynamics - Effective collaboration in AI development

What You Get

  • Working AI prototype with full documentation
  • Complete source code and data pipelines
  • Performance metrics and validation results
  • Production roadmap with effort estimates
  • Technical architecture documentation
  • Stakeholder feedback compilation
  • Sprint methodology playbook for future projects
  • Certificate of completion