System Integration Architecture Documentation

This document outlines the architecture for system integration, focusing on core integration points, integration patterns, and error handling mechanisms.

Core Integration Points

ML System Integration

  • Data Pipeline Integration: Describes how data flows through various stages from collection to processing.
  • Model Registry Integration: Details the management and versioning of machine learning models.
  • Metrics Collection Integration: Explains the collection and aggregation of performance metrics.

  • Validation System Integration: Covers the validation processes to ensure data and model integrity.

  • Data Ingestion: Describes the process of collecting data from various sources.

  • Data Preprocessing: Details the process of cleaning, transforming, and preparing data for analysis.
  • Model Training: Covers the process of training machine learning models on the preprocessed data.
  • Model Evaluation: Describes the process of evaluating the performance of trained models.
  • Model Deployment: Details the process of deploying trained models to production.

Blockchain Integration

  • Bitcoin Core Connection: Integration with the Bitcoin Core for blockchain operations.
  • Lightning Network Interface: Interface for handling transactions on the Lightning Network.
  • DLC Protocol Support: Support for Discreet Log Contracts (DLC) for smart contracts.
  • RGB Asset Management: Management of assets using the RGB protocol.

  • Stacks Smart Contracts: Integration with Stacks blockchain for smart contract execution.

  • Transaction Management: Describes the process of managing transactions on the blockchain.

  • Block Management: Details the process of managing blocks on the blockchain.
  • Smart Contract Execution: Covers the process of executing smart contracts on the blockchain.

Web5 Integration

  • DID Management: Handling Decentralized Identifiers (DIDs) for identity management.
  • Data Storage: Mechanisms for storing data in a decentralized manner.
  • Protocol Handling: Managing various protocols for data exchange.

  • State Management: Maintaining the state of the system across different components.

  • Identity Verification: Describes the process of verifying user identities using DIDs.

  • Decentralized Data Storage: Details the process of storing data in a decentralized manner.
  • Data Encryption: Covers the process of encrypting data for secure storage and transmission.
  • Data Authentication: Describes the process of authenticating data to ensure its integrity.

Integration Patterns

  1. Data Collection: Gathering data from various sources.
  2. Validation: Ensuring data integrity and correctness.
  3. Processing: Transforming and analyzing data.
  4. Storage: Storing data in databases or other storage systems.
  5. Analysis: Analyzing stored data to derive insights.

Control Flow

  1. Request Handling: Managing incoming requests.
  2. Authentication: Verifying user identities.
  3. Authorization: Granting access based on permissions.
  4. Execution: Performing the requested operations.
  5. Response: Sending back the results of the operations.

Error Handling

  1. Error Detection: Identifying errors in the system.
  2. Error Classification: Categorizing errors based on severity and type.
  3. Error Recovery: Implementing mechanisms to recover from errors.
  4. Error Reporting: Logging and reporting errors for further analysis.
  5. Error Analysis: Analyzing errors to prevent future occurrences.