You are missing a lot of things that you don't know. If you want to do machine learning at some point you have to train a model. You need to know how to clean the data, how to create the train/validation/test set, how to measure how good your model is, how to compare to other models you trained previously. If the model is not performing correctly you need to know why. You need to know the trade offs between precision and recall. This is like 95% of your work, the other 5% is running the training in Amazon or whatever you want to use.

I have worked with people who get a training example code and apply it to a dataset. And few weeks later they were still pulling off their hair because the model wasn't working in production but they have such a great results in their test. I took a look to their way of doing the training and I could point to so many errors they were doing why the model will never work in production.

That is not cutting edge, but at some point there is a new model that works better, and you should understand why in order to improve you current model. So probably you will have to read the paper and understand it.