I have a couple questions:

1. Is MLOps in some way a "full stack data scientist" or combination with machine learning engineer Since it includes the deploying part? Because I'm interested in both ML/DL and the deploying part of it(eg with a flask API), and I don't really know any course/book for it, but for MLOps.
2. How do I know what algorithm to use for a specific problem? eg a categorization problem. How do I know if i have to use a SVM, a Multiple Logistic Regression, a KNN or a Bayesian algorithm? Do I really need to study deeply the algorithms, or I can use a cheatsheet, or maybe just learn trying them all and see which one gives me better results?
3. Is it possible to get a part time Data Science/Machine Learning engineering/MLOps job? Because I'm kinda in the middle of my university studies, and unfortunately I need to work (Actually I'm a Data Engineer, but I want to transition to one of those roles), and studying and working full time at the same time, is pretty difficult.