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Eugene Yan and Practical AI discuss testing ML systems and code

By PulseAugur Editorial · [2 sources] ·

Eugene Yan's article details a comprehensive approach to testing machine learning systems, differentiating between traditional software tests and ML-specific tests. ML tests are further categorized into pre-train tests for implementation correctness, post-train tests for expected learned behavior, and evaluation metrics for performance assessment. The author uses a DecisionTree implementation and the Titanic dataset to demonstrate these testing methodologies, incorporating practices like unit testing, code coverage, linting, and type checking. AI

RANK_REASON The cluster discusses a technical blog post and podcast episode detailing methods for testing machine learning code and systems, which falls under research and development practices.

Read on Practical AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Eugene Yan and Practical AI discuss testing ML systems and code

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The cluster discusses a technical blog post and podcast episode detailing methods for testing machine learning code and systems, which falls under research and development practices.
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2434 days old
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COVERAGE [2]

  1. Eugene Yan TIER_1 English(EN) ·

    How to Test Machine Learning Code and Systems

    Checking for correct implementation, expected learned behaviour, and satisfactory performance.

  2. Practical AI TIER_1 English(EN) · Practical AI LLC ·

    Testing ML systems

    <p>Production ML systems include more than just the model. In these complicated systems, how do you ensure quality over time, especially when you are constantly updating your infrastructure, data and models? Tania Allard joins us to discuss the ins and outs of testing ML systems.…

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