In this episode, we dig deep into the unglamorous side of AI and computer vision projects — the mistakes, misfires, and blind spots that too often derail even the most promising teams. Based on BigVision.ai’s playbook “Common Pitfalls in Computer Vision & AI Projects”, we walk through a field-tested catalog of pitfalls drawn from real failures and successes. We cover: Why ambiguous problem statements and fuzzy success criteria lead to early project drift The dangers of unrepresentative training data and how missing edge cases sabotage models Labeling mistakes, data leakage, and splits that inflate your offline metrics The trap of being model-centric instead of data-centric Shortcut learning, spurious correlations, and how models “cheat” Misaligned metrics, thresholds, and how optimizing the wrong thing kills business impact Over-engineering vs. solid baselines The ambition vs. reproducibility tension (drift, code, data versioning) Deployment constraints, monitoring, silent failures, and how AI degrades in the wild Fairness, safety, adversarial robustness, and societal risks Human factors, UX, privacy, compliance, and integrating AI into real workflows ROI illusions: why model accuracy alone doesn’t pay the bills We also reveal their “pre-flight checklist” — a lean but powerful go/no-go tool to ensure your project is grounded in real needs and avoids death by scope creep. Why listen? This isn’t theory — it’s a survival guide. Whether you’re a founder, ML engineer, product lead, or AI skeptic, you’ll pick up concrete lessons you can apply before you spend millions. Avoiding these traps could be the difference between shipping a brittle proof-of-concept and deploying a real, reliable system that delivers value. Tune in for cautionary tales, war stories, and actionable tactics you can steal for your next vision project. Resources https://bigvision.ai/pitfalls [PDF] Big Vision LLC - Computer Vision and AI Consulting Services. OpenCV University - Start your AI Career today!
