Interactive computational notebooks (e.g., Jupyter notebooks) are widely used in machine learning engineering (MLE) to program and share end-to-end pipelines, from data preparation to model training and evaluation. However, environmental erosion-the rapid evolution of hardware and software ecosystems for machine learning-has rendered many published MLE notebooks non-reproducible in contemporary environments, hindering code reuse and scientific progress. To quantify this gap, we study 12,106 notebooks selected from 75 popular Kaggle competitions: only 26% remain reproducible today. Crucially, we find that environment backporting, i.e., downgrading dependencies to match the submission time, does not improve reproducibility (decreased to 12%) but rather introduces additional failure modes. To address environmental erosion, we design and implement MLEModernizer, an LLM-driven agentic framework that treats the contemporary environment as a fixed constraint and modernizes notebook code to restore reproducibility. MLEModernizer iteratively executes notebooks, collects execution feedback, and applies three types of targeted fixes: error-repair, runtime-reduction, and score-calibration. Evaluated on 8,210 notebooks that are non-reproducible under the baseline environment, MLEModernizer makes 3,292 (40.1%, GPT-5.2) and 3,683 (44.9%, GPT-OSS-120b) notebooks reproducible. MLEModernizer presents a best-effort automated recovery and modernization technique that can improve reproducibility for a subset of notebooks. Practitioners can leverage MLEModernizer to validate, reuse, and maintain MLE artifacts as the hardware and software ecosystems continue to evolve.

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