If you are a data scientist, or data analyst who started and completed a promising ML solution that needs to be prepared for production and wonder how you can do it by yourself, this article is for you.
This is really great, breaking it down piece by piece. Starting with such a simple example, and having the reader understand how much scaffolding can go around these simple examples. Definitely a great way to grasp what's going on in the whole situation, from importing the data to VC and Logging. Please never send me a .py or a .zip and then go tell me where to find the data. We are in 2023. Love it :D
It's all about the programming paradigm you want to use. In the case of OOP, you represent a software product as a collection of objects that have certain properties and methods and that interact with each other.
It all depends on your project, the programming language you use, etc.
This is really great, breaking it down piece by piece. Starting with such a simple example, and having the reader understand how much scaffolding can go around these simple examples. Definitely a great way to grasp what's going on in the whole situation, from importing the data to VC and Logging. Please never send me a .py or a .zip and then go tell me where to find the data. We are in 2023. Love it :D
As a MLOPs, I would thank you for doing all this before you hand over the code to me.
What is the difference between only writing functions and moving to classes?
It's all about the programming paradigm you want to use. In the case of OOP, you represent a software product as a collection of objects that have certain properties and methods and that interact with each other.
It all depends on your project, the programming language you use, etc.