Scikit-learn decision tree in production
Persisting the model parameters is the only out of the box solution in sklearn. You should ensure that:


Your model is reproducible from data
The versions of dependencies used (incl sklearn) is locked
Your QA system checks correctness of your model 


If you have these things down then the possible changes in model persistence in sklearn is a non-issue. If it happens, you just update sklearn in a controlled manner, retrain and deploy as usual.
And you avoid a whole range of other problems, for instance bugs in sklearn/numpy/whatever catching you by suprise. Or new data needs new models trained and deployed.

You can of course build your own persistence mechanism but you then need to maintain the compatibility. Better spend the effort one the above.