An hour spent improving your Software Engineering skills is mo | Artificial Intelligence

An hour spent improving your Software Engineering skills is more productive than an hour spent improving your Machine Learning skills.

I’m not saying ML is unimportant. Obviously it is.

I’m just saying I've seen more Data Scientists held back by their ability to deploy working software than by their ability to make proper modeling decisions.

Any Data Scientist can build a model in a Jupyter Notebook.

Fewer can take that model and deploy it in a production setting.

Even fewer can do this in a way that's is fault tolerant, scales well, and allows for easy iteration.

A strong understanding of SWE principles lets you build and deploy your models more efficiently and autonomously, which will better differentiate you from other Data Scientists.

Here's a shortlist of a few software engineering concepts I've found to be relevant with DS and ML:
1) REST and Micro-service architecture
2) Version control & CI/CD
3) Dependency injection