[Verse 1] Started with a notebook, chaos everywhere Code scattered wildly, data who knows where No version control, experiments lost in time Can't reproduce results, it's such a paradigm crime But there's a better way to build ML right Structure and standards bring order to sight [Chorus] Track your experiments, MLflow's the key Weights and Biases for what you need to see Neptune records every single run Data pipelines clean, your work is never done Avoid the leakage, test your model well Engineering best practices, stories they tell [Verse 2] Cookiecutter templates give you structure true Folders organized for me and you ETL design with feature stores in place Data contracts flowing at a steady pace Great Expectations testing every byte Documentation clear makes everything bright [Chorus] Track your experiments, MLflow's the key Weights and Biases for what you need to see Neptune records every single run Data pipelines clean, your work is never done Avoid the leakage, test your model well Engineering best practices, stories they tell [Bridge] Model cards document what your system does README standards help because Decision logs track why you chose this way Target leakage ruins your training day Train-test contamination breaks your score Unit tests protect what you're building for [Verse 3] Reproducible results for collaboration Version everything across your organization From messy notebook to production grade Quality engineering, that's how success is made Anti-patterns lurking, watch for data drift Best practices give your projects a lift [Chorus] Track your experiments, MLflow's the key Weights and Biases for what you need to see Neptune records every single run Data pipelines clean, your work is never done Avoid the leakage, test your model well Engineering best practices, stories they tell [Outro] Structure reproducibility Implement reliability Common pitfalls you'll avoid ML engineering deployed
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