TensorFlow

TensorFlow is an open-source machine learning framework developed by Google. It is designed for building and deploying machine learning models at scale, with particular strengths in computational efficiency and flexibility.

How TensorFlow Works:

1. Data Processing Pipelines: TensorFlow uses tf.data to create efficient pipelines for ingesting and preprocessing both structured and unstructured data. This enables Trumnix to handle diverse data inputs, including on-chain transaction logs and off-chain user profiles.

   Example:

   • For credit scoring, TensorFlow processes blockchain data (wallet activity, transaction volume) and integrates it with off-chain signals (social scores, utility payments).

   • Data pipelines can preprocess this information (e.g., normalizing wallet balances, tokenizing text) before feeding it into the models.

2. Model Architecture: TensorFlow supports building complex deep learning architectures using tools like tf.keras. For Trumnix, these architectures include:

   • Fully Connected Neural Networks (FCNN) for credit scoring, where each layer refines borrower profiles to generate a dynamic score.

   • Recurrent Neural Networks (RNN) for sequential data, like analyzing borrower repayment patterns over time.

3. Deployment: TensorFlow’s TensorFlow Serving allows models to be deployed in a production environment for real-time inference.

   • Example: Credit scores are updated dynamically every time new borrower data becomes available.

Use Case in Trumnix: TensorFlow powers the credit scoring engine, where it processes data streams and applies machine learning algorithms to dynamically adjust credit scores. This real-time adaptability ensures that borrowers are fairly assessed and loan terms remain relevant to current conditions.