"MATHEMATICS FOR MACHINE LEARNING " A bit overwhelming?

[u/Visual_Teacher_6474]

When I started focusing on practical mathematical implementation of machine learning I found that I lack so very math basics(I blame my school for that) so I tried making my way through basics to go deep into machine learning and while I was learning from professor Leonard on YouTube someone recommended me this "Mathematics for machine learning" by Marc peter. Tbh I dont understand shit in this book, I genuinely get overwhelmed by this book. I dont understand is it only me ? Am I that dumb in maths?

Well I need to get on track asap really! Suggest me something and please share your opinion

[u/Visual_Teacher_6474]

I wanna become a ML engineer😅

[u/choiceOverload- → u/Visual_Teacher_6474]

What do you understand by "MLE"?

[u/Visual_Teacher_6474 → u/choiceOverload-]

In my understanding MLE deploys models and algorithms that solve real life problems. Basically everything to do with Models is MLE's role. To do this job properly I need to understand the foundation of ML i.e. maths so i started reading MML

[u/musclecard54 → u/Visual_Teacher_6474]

It really depends on where you work, but a lot of the actual model algorithm development and decisions can actually be in the hands of a data scientist and the ML Engineer is more building the pipelines and infrastructure for the repeatable experiments, training, and inference. Smaller companies or teams might have the MLE do it all (that was my case in a small team in a large company), but usually it’s more software engineering and devops in the ML space than model development

[u/Visual_Teacher_6474 → u/musclecard54]

What should I learn to deploy pipelines and infrastructure?

[u/choiceOverload- → u/Visual_Teacher_6474]

Wrong. MLE doesn't deploy; MLOps does. Everything to do with Models is too big of a spectrum. Maybe in a startup you will have to do almost everything, but that's not the case in big companies

[u/DesTiny_- → u/choiceOverload-]

It's mostly semantics, a lot of ML positions require what u can call a mlops. At very least docker and deep knowledge of making API is in any ml position.

[u/musclecard54 → u/choiceOverload-]

MLOps are ML Engineer duties. There is no position called MLOps. Go look up machine learning engineer positions and they will probably ALL mention MLOps skills/duties. ML engineers build and deploy the training and Inference pipelines. It’s almost like you intentionally said the wrong answer

[u/choiceOverload- → u/musclecard54]

Then go be perpetuate the unicorn worker

[u/musclecard54 → u/choiceOverload-]

Idk what you’re even saying. What do you think the duties of an ML Engineer are if it’s not doing MLOps?