在传统机器学习工作流中，开发者常陷入三个典型困境：实验过程缺乏可视化反馈（"黑箱训练"）、参数调整效率低下（"手动改参重启"）、知识资产难以沉淀（"实验复现难"）。这些痛点导致高达73%的模型迭代时间浪费在非核心环节（数据来源：2024年ML工程效率报告）。

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