RPC vs RESTful
两种方式，并非一定孰优孰劣，主要是看那种抽象更适合项目的抽象！正如编程范式，不只有OOP还有FP!
也说明不管何种抽象都是大千世界某种角度的抽象和假设，这个假设适合所有场景吗？在各场景都好用吗？
因此对于使用者而言最重要的就是把握好各种假设的适用场景及其该条件下的优劣！对，所有模型都是假的，
没有一个模型能方便准确的概括一切！明确定义自己的业务很重要！
https://towardsdatascience.com/deploying-a-machine-learning-model-as-a-rest-api-4a03b865c166
https://becominghuman.ai/creating-restful-api-to-tensorflow-models-c5c57b692c10
https://zhuanlan.zhihu.com/p/52096200
https://www.hardikp.com/2018/07/28/services/
https://www.cnblogs.com/jager/p/6519321.html
https://blog.csdn.net/douliw/article/details/52592188
https://mbd.baidu.com/newspage/data/landingsuper?context=%7B%22nid%22%3A%22news_10498150650081469485%22%7D&n_type=0&p_from=1
https://medium.freecodecamp.org/a-beginners-guide-to-training-and-deploying-machine-learning-models-using-python-48a313502e5a
https://towardsdatascience.com/there-are-two-very-different-ways-to-deploy-ml-models-heres-both-ce2e97c7b9b1
https://www.quora.com/How-do-you-take-a-machine-learning-model-to-production
https://hackernoon.com/deploy-a-machine-learning-model-using-flask-da580f84e60c
https://blog.hyperiondev.com/index.php/2018/02/01/deploy-machine-learning-model-flask-api/
https://towardsdatascience.com/a-flask-api-for-serving-scikit-learn-models-c8bcdaa41daa
https://www.analyticsvidhya.com/blog/2017/09/machine-learning-models-as-apis-using-flask/