Eval-Driven Agentic AI Development: The Most Important Practice Nobody Is Doing (And What I Got…
📰 Medium · Machine Learning
Learn to build automated CI/CD evaluation gates for AI systems using real production failures, not assumptions, to improve AI development
Action Steps
- Build automated CI/CD pipelines using tools like Jenkins or GitLab CI/CD
- Configure evaluation gates to test AI systems against real production failures
- Run simulations to validate AI system performance under various scenarios
- Test AI systems using real-world data and feedback loops
- Apply continuous integration and delivery principles to AI development
Who Needs to Know This
AI engineers and developers can benefit from this practice to ensure reliable and efficient AI system deployment, while product managers can use it to improve overall product quality
Key Insight
💡 Using real production failures to build automated CI/CD evaluation gates can significantly improve AI system reliability and efficiency
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🚀 Improve AI development with automated CI/CD evaluation gates using real production failures #AI #CI/CD
Key Takeaways
Learn to build automated CI/CD evaluation gates for AI systems using real production failures, not assumptions, to improve AI development
Full Article
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