Technical Foundations of MCP (Model Capture Protocol) 📦

Advanced

MCP is designed for robust, standardized model deployment and exchange, focusing heavily on model integrity, versioning, and compatibility across platforms. It often involves protocols like PMML, ONNX, or proprietary standards that define how models are serialized and transferred.

Core Features of MCP:

  • Model Serialization: Converts models into a transportable format.
  • Versioning & Compatibility: Ensures different versions work seamlessly.
  • Security & Integrity: Implements checksums, encryption for secure transfer.
  • Interoperability: Supports multiple frameworks (e.g., TensorFlow, PyTorch).

Example use case: Deploying a trained neural network from a development environment to an edge device securely and reliably.

Diagram:

flowchart LR
A[Train Model (TensorFlow)] --Serialize via MCP--> B[Model Repository]
B --Deploy via MCP--> C[Edge Device]