Evals: What Software Engineers Should Learn From Data Scientists

📰 Medium · Data Science

Software engineers can learn from data scientists by incorporating evaluation metrics and iterative development into their workflow to improve AI model deployment and production readiness

intermediate Published 8 May 2026

Action Steps

Adopt evaluation metrics to measure AI model performance
Implement iterative development to refine models based on feedback
Collaborate with data scientists to integrate evaluation metrics into the development workflow
Use data science techniques to monitor and improve model performance in production
Apply agile development principles to facilitate rapid iteration and improvement

Who Needs to Know This

Software engineers and data scientists can collaborate to improve the production readiness of AI models by adopting evaluation metrics and iterative development, leading to more successful deployments

Key Insight

💡 Incorporating evaluation metrics and iterative development into the software engineering workflow can significantly improve AI model production readiness

Key Takeaways

Software engineers can learn from data scientists by incorporating evaluation metrics and iterative development into their workflow to improve AI model deployment and production readiness

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Title: Evals: What Software Engineers Should Learn From Data Scientists

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Published Time: 2026-05-08T13:02:38Z

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# Evals: What Software Engineers Should Learn From Data Scientists | by Daniel Riad | May, 2026 | AI Advances

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Evals: What Software Engineers Should Learn From Data Scientists

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