The following AI/ML use cases will help you understand how to use TripleBlind to conduct privacy-preserving modeling and inferencing on 3rd-party data.
AI/ML Use Case #1: Model Training
Using APIs, train an AI/ML model based on datasets of virtually any type. Personas represented in this use case are: Data Scientist (User), Dataset Owner.
The following workflow is used to train models using TripleBlind.
- Initialize a TripleBlind session
- Register new assets or locate existing assets
- Explore assets
- Perform preprocessing and tune model parameters
- Train the model and get results of the training run
To execute this use case follow these steps in your Python IDE:
1. The User authenticates with Router and starts a Session.
import tripleblind as tb tb.initialize(api_token=user1_token)
ℹ️ The call to
tb.initialize is unnecessary if the User token is set up in the User’s
2. The Owner and/or User registers datasets as new assets, or the User searches for existing assets and selects them. The first code snippet is an example of registering a new dataset asset. The second is an example of searching for an existing asset.
asset0 = tb.Asset.position( file_handle="/Users/john/data_munge_sql_a.csv", name="Data Munge Table A-001", desc="Example dataset containing patient information in imperial units.", is_discoverable=True, ) asset0 = tb.TableAsset.find("Data Munge Table A")
3. Optionally, the User explores an EDA profile and synthetic data view of registered Assets.