AI/Machine Learning

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.

Workflow

The following workflow is used to train models using TripleBlind.

  1. Initialize a TripleBlind session
  2. Register new assets or locate existing assets
  3. Explore assets
  4. Perform preprocessing and tune model parameters
  5. Train the model and get results of the training run

Steps

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 tripleblind.yaml file.

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.