Which custom model training approach supports training data privacy and version control?

The approach is fine-tuning with managed dataset/versioning support—typically in a platform that provides private training data storage and dataset version control.

If you’re asking in the context of custom model options, the key characteristics you want are:

• Training data privacy: your data is kept private and not used to train other models
• Version control: you can track and reproduce different dataset/model versions

If you want, I can also compare this with other custom-training approaches like prompting, RAG, fine-tuning, and full training.