Eval-Driven Agentic AI Development: The Most Important Practice Nobody Is Doing (And What I Got…
📰 Medium · DevOps
Learn to build automated CI/CD evaluation gates for AI systems using real production failures, not assumptions, to improve reliability and efficiency
Action Steps
- Build automated CI/CD evaluation gates for AI systems using real production failures
- Run simulations to test the gates with various failure scenarios
- Configure the gates to trigger alerts and notifications for potential issues
- Test the gates with real-world data to ensure accuracy and reliability
- Apply the evaluation gates to existing AI systems to improve reliability and efficiency
Who Needs to Know This
DevOps and AI engineers can benefit from this practice to ensure smooth and reliable deployment of AI systems, and to reduce errors and downtime
Key Insight
💡 Using real production failures to build evaluation gates can significantly improve the reliability and efficiency of AI systems
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🚀 Improve AI system reliability with automated CI/CD evaluation gates! 🚀
Key Takeaways
Learn to build automated CI/CD evaluation gates for AI systems using real production failures, not assumptions, to improve reliability and efficiency
Full Article
Moving from 20 to 200 cases. How to build automated CI/CD evaluation gates for AI systems using real production failures, not assumptions. Continue reading on Predict »
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