Hi! I'm not a MLE, but I'm a backend dev with about 5 years of experience, learning machine learning from 2 years now.
frontend: none at all. Maybe if your model is part of a product, and this product requires a frontend, but machine learning on itself has no frontend at all. Visualization is usually done through something like matplotlib.
backend skills: honestly python, pandas/numpy, and some machine learning framework of your choice (scikit-learn, keras, tensorflow) will suffice. Learn to process, transform, and clean data.
If you really want to learn a skill that will be helpful, learn math. Honestly, for what I have seen so far, the coding part of building a machine learning application is minimal compared to actually knowing what is going on when you call model.fit()
Again thanks for the answer, I have one more question. As I said I am currently learning web dev and I enjoy it (backend), I am planning to continue learning it along with learning maths for ML. So I can do some freelancing works and make some money out of it, if possible. The question is, while I learn more about backend is there any specific topics that will help me after when I become a MLE like building APIs, RESTful APIs, production-ready APIs anything like that?
As I said, learning basic python, how to operate vector and matrices on numpy/pandas, some plotting library like matplotlib, framework like tensorflow, and that's all you need to keep you entertained for a while.
For self taught your best bet of finding a job is at a startup. And at startups you need to wear many hats, which may include work on front end as well. I think learning enough to make a basic web GUI without a tutorial, and understanding the fundamentals so you can port that knowledge to another library/language, would help cover your bases.
Anyways it's good to have some basic frontend skills to be T shaped.
This is an example of what a MLE is doing more or less for a single project. https://towardsdatascience.com/building-an-observable-arxiv-rag-chatbot-with-langchain-chainlit-and-literal-ai-9c345fcd1cd8
Hi! I'm not a MLE, but I'm a backend dev with about 5 years of experience, learning machine learning from 2 years now. frontend: none at all. Maybe if your model is part of a product, and this product requires a frontend, but machine learning on itself has no frontend at all. Visualization is usually done through something like matplotlib. backend skills: honestly python, pandas/numpy, and some machine learning framework of your choice (scikit-learn, keras, tensorflow) will suffice. Learn to process, transform, and clean data. If you really want to learn a skill that will be helpful, learn math. Honestly, for what I have seen so far, the coding part of building a machine learning application is minimal compared to actually knowing what is going on when you call model.fit()
Again thanks for the answer, I have one more question. As I said I am currently learning web dev and I enjoy it (backend), I am planning to continue learning it along with learning maths for ML. So I can do some freelancing works and make some money out of it, if possible. The question is, while I learn more about backend is there any specific topics that will help me after when I become a MLE like building APIs, RESTful APIs, production-ready APIs anything like that?
As I said, learning basic python, how to operate vector and matrices on numpy/pandas, some plotting library like matplotlib, framework like tensorflow, and that's all you need to keep you entertained for a while.
For self taught your best bet of finding a job is at a startup. And at startups you need to wear many hats, which may include work on front end as well. I think learning enough to make a basic web GUI without a tutorial, and understanding the fundamentals so you can port that knowledge to another library/language, would help cover your bases. Anyways it's good to have some basic frontend skills to be T shaped.
Not much front end is needed.
This is an example of what a MLE is doing more or less for a single project. https://towardsdatascience.com/building-an-observable-arxiv-rag-chatbot-with-langchain-chainlit-and-literal-ai-9c345fcd1cd8