binary classification pipeline to select threshold

There are quite a few questions regarding the optimisation of binary threshold in a classification problem. However, I haven't found a single end-to-end solution to this problem.

In an existing project, I have come up with the following pipeline to train a binary classifier:

1. Outer-CV due to small to moderate data size.
   1. Inner-CV to tune hyperparameters
   2. Train model with tuned hyperparameters on outer-cv trainset
   3. Predict on the outer-cv test set
   4. Find optimal threshold using prediction probabilities
   5. Get score converting prediction probabilities to class with the optimal threshold
2. Report avg/std scores along with thresholds

Since there's tiny to no deviation on the score across different folds. (However, the optimal threshold stddev is 3.2)

3. Tune hyperparameter on entire data
4. Train model with tuned hyperparameters on entire data

Now my questions are:

1. Is this pipeline reasonable/correct? i.e., have I missed anything or parts are unnecessary?
2. How to get the final optimal threshold for my model when predicting in production.