Machine Learning models in production environment
I think this is a good approach in general. However:


Fine-tuning your model (online learning) depends a lot on the algorithm and model how well this works. Depending on your algorithm it might be wise to retrain the whole thing
Your sample space might change overtime. If you have enough data maybe retraining every few days/weeks/months over only the last year worth of data might be better. If your old samples don't represent the current situation as well having them included might hurt your performance more than the extra samples help
The biggest condition is if it's tested and how much downtime it involves, but in general swapping more times is better, and this can be automated