请问HN：在训练机器学习模型时，您是如何处理日志记录和评估的？
大家好，我目前正在参加几门机器学习课程，虽然它们在理论方面做得很好，但在应用方面的内容却很少，至少没有超过一些基本的Jupyter Notebook实现。
我一直遇到的一个问题是如何处理模型的日志记录和评估。目前我使用Jupyter Notebook来训练模型，然后用测试集生成几个不同指标的图表。
这个工作流程似乎是我所在项目组的标准做法，但我总觉得这看起来有些依赖直觉且不够优化。
我有几个项目即将开始，我想借此机会改善我的模型训练方法。你们使用什么方法？有没有推荐的文章或库？你们希望初级工程师了解哪些内容？
谢谢！
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Hi all, I'm currently in a few ML classes and, while they do a great job covering theory, they don't cover application. At least not past some basic implementations in a Jupyter Notebook.<p>One friction point I keep running into is how to handle logging and evaluation of the models. Right now I'm using Jupyter Notebook, I'll train the model, then produce a few graphs for different metrics with the test set.<p>This whole workflow seems to be the standard among the folks in my program but I can't shake the feeling that it seems vibes-based and sub optimal.<p>I've got a few projects coming up and I want to use them as a chance to improve my approach to training models. What method works for you? Are there any articles or libraries that you would recommend? What do you wish Jr. Engineers new about this?<p>Thanks!