16 Aug: My second article: From Scripts To Prediction API

8月16日：我的第二篇文章：从脚本到预测API

As advanced beginners, we know quite a lot: EDA, ML concepts, model architectures etc…… We can write a big Jupyter Notebook, click “Restart & Run All” and generate state-of-art results. OK, what next? How can others use/reproduce/scale your results?

作为高级初学者，我们了解很多：EDA，ML概念，模型体系结构等……我们可以编写一个大型的Jupyter Notebook，单击“重新启动并全部运行”并生成最新的结果。好，接下来呢？其他人如何使用/复制/缩放结果？

In this article, I will show you how I convert a Jupyter Notebook from Kaggle on the Wine dataset to scripts, share some engineering practices that we can adapt easily and bring benefits right away.

在本文中，我将向您展示如何将Jupyter Notebook从Wine数据集上的Kaggle转换为脚本，分享一些我们可以轻松适应并立即带来收益的工程实践。

For each of them, there is much more to dig in but we will only surface with the basics, show the benefits, and explain the thought process! The goal is to minimize the probability that the engineers roll their eyes when they talk to you.

对于他们每个人，还有很多需要挖掘的东西，但是我们只会浮现基础知识，展示收益并解释思想过程！目的是最大程度地降低工程师与您交谈时翻白眼的可能性。

Jupyter笔记本 (Jupyter Notebook)

Jupyter Notebook is nice for exploration and prototype but we cannot stay there forever. No engineers [Or no one] wants to read your code and rewrite it. Even you are fortunate enough to have engineering resources, it just slows the whole process down

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