{"id":301,"date":"2021-07-26T00:00:00","date_gmt":"2021-07-26T00:00:00","guid":{"rendered":"https:\/\/tac.debuzzify.com\/?p=301"},"modified":"2023-06-27T23:57:15","modified_gmt":"2023-06-27T23:57:15","slug":"python-cli-tutorial","status":"publish","type":"post","link":"https:\/\/www.the-analytics.club\/python-cli-tutorial\/","title":{"rendered":"How to Create Interactive CLIs in Python?"},"content":{"rendered":"\n\n\n
It was a terrible assumption. I thought that once deployed, it was over. But, deployment is only the beginning for most data science projects.<\/p>\n\n\n\n
Frequently, we have to retrain and update the model. Often with new data, sometimes with a different configuration, and occasionally with a unique architecture altogether.<\/p>\n\n\n\n
To make things worse, sometimes, you hand it over to another team or a client who doesn\u2019t have the technical skills to do it. If not, whoever is doing these maintenance may have a different understanding of the architecture.<\/p>\n\n\n\n
In such cases, we build a web portal to support aftercare. We link the app to the data stores, let the user do configurations through a web form, and run the algorithms.<\/p>\n\n\n\n
Building a web app to interact with your machine learning models<\/a> is not a bad idea. Especially tools such as Streamlit allows data scientists to create web apps<\/a> without a single line of HTML, CSS, or JavaScript.<\/p>\n\n\n\n Yet, a web app isn\u2019t the right<\/a> fit for a few. Say you have concerns about hosting the web app<\/a>. Don\u2019t worry; it\u2019s not a dead end. We have a fallback option that is indeed a robust solution for the problem.<\/p>\n\n\n\n You can create a command-line interface (CLI) to productize and deliver your machine-learning projects.<\/p>\n\n\n\n