what are the next step after ML prediction and how to proceed?
If one understands the business problem you are trying to solve, then this shouldn't be that hard.


You can present the results of your model. Understand your data first, is it skewed, balanced? Understand your model, check the feature importance. Do they make sense with the domain knowledge you have? For example, if your data is highly imbalanced and you have accuracy of 0.99, then it's highly likely that your model is classifying almost everything as one class. Try and make sense of the model, may be try running some shallow models to establish a baseline.
There's no free lunch. You can't have frameworks for interpreting results, you can have a good set of metrics that you can use but metrics change based on how your data is.
Model is learning continuously?? You mean re-training the model. You need to establish a pipeline for it. You can have a python project for this or use DAG mechanisms like AirFlow.
There's no definite guide, a few text books might help but this comes with experience and understanding the concepts of machine learning thouroughly.