As developers, we love diving into the latest AI frameworks and algorithms, but I've got some uncomfortable news: 80% of AI projects fail, and it's probably not what you think.
After working as a QA architect on multiple AI implementations, I've seen the pattern over and over. Companies spend millions on GPUs, hire PhD researchers, and use cutting-edge models... only to fail spectacularly in production.
The culprit? Data quality.
I just published a deep dive into this issue, covering:
• Real case studies (including the fascinating "10:10 clock phenomenon")
• Training data bias that creates systematic failures
• QA frameworks specifically designed for AI/ML systems
• Actionable strategies to prevent costly failures
The hard truth: We're so focused on algorithmic sophistication that we're ignoring the foundation everything is built on.
🔗 Read the full analysis:
The $100 Million AI Mistake: Why Data Quality Matters More Than Algorithms | by Sohail Mohammed | Aug, 2025 | Generative AI
80% of AI projects fail due to poor data quality. Read here to learn from real case studies and implement QA architect frameworks to build…
generativeai.pub
Discussion questions:
• Have you encountered data quality issues in your ML projects?
• What QA practices do you use for AI/ML systems?
• How do you handle training data validation?
Let's discuss! Would love to hear your experiences and solutions.
#ai #machinelearning #datascience #qualityassurance #softwareengineering #beginners
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