Mongo works well as a straight-up JSON store for store-and-retrieve use cases (with no analytics). It is horizontally scalable, avoids the overhead of relational databases, has indexing capabilities, and provides a strong consistency model. The big improvement came with WiredTiger, which addressed many of the issues that plagued earlier versions of Mongo.
I've seen high-speed machine data stored in Mongo for logging and visualization purposes. It's an improvement over writing csv files to disk.
However, if you ever need to perform non-trivial analytics, Mongo's weaknesses quickly become obvious. For machine learning, typically you would want to first ETL the data into a dataframe-like structure (which is a structure native to SQL databases).
I've seen high-speed machine data stored in Mongo for logging and visualization purposes. It's an improvement over writing csv files to disk.
However, if you ever need to perform non-trivial analytics, Mongo's weaknesses quickly become obvious. For machine learning, typically you would want to first ETL the data into a dataframe-like structure (which is a structure native to SQL databases).