Challenges with high cardinality data
You can use replace all id numbers and names in the data with a standard token like <ID> or <NAME>. This should be done during preprocessing. Next you should pick a fixed vocabulary. Like all words that occur at least 5 times in the training data.
Background: I am working on classifying data from a ticketing system data into a failed or successful requests. A request goes into various stages before getting completed. Each request is assigned to different teams and individuals before being marked as complete. 
Making use of historical data I want to create predictions for these tickets at a final state x before they are marked as complete(success or fail).
Amongst the various features, individual's name who work on the records & team names are very important factors in analysing this data. Being a huge organization I expect 5-10 new names being added every day.
Historical data 
60k records (used for training, validation and testing)
Has 10k unique individual names 
Current data
Overal 1k records
- Has 200 individual names
I'm facing a challenge due to high cardinality data like individual names whose number is not fixed and keeps on growing.
1. Challenge while making actual predictions - The no. of columns for the current data will be different every time and would never match the feature length of training data. 
- So I have to train my model every single time, I want to make predictions.
2. Challenge while data prep -  The above also presents a challenge for data prep as now I always have to encode the complete data and the query encoded data to split into current and future data.
Sorry for the long story. 
What am I looking for?
Is there a better way to approach? 
These high & constantly changing dimensions is a pain. Any suggestions on how can I handle them, to avoid training every time?
Note: I tried using PCA and Autoencoders for dim red. (The results were not great for my highly unbalanced dataset so I'm working on the data with high dimensions only)