Mlops_model_control_plane_server

Created By
Hsinghsudwala year ago
MLOps workflow with a Model Control Plane (MCP) server for model management and deployment
Overview

what is MLOps Model Control Plane Server?

MLOps Model Control Plane Server is a comprehensive solution for managing and deploying machine learning models through an MLOps workflow.

how to use MLOps Model Control Plane Server?

To use the server, clone the repository, install the dependencies, configure the settings, and run the application using provided commands for data processing, model training, and inference.

key features of MLOps Model Control Plane Server?

  • Data processing and model training pipelines
  • Model registry for versioning and lifecycle management
  • FastAPI-based Model Control Plane (MCP) server
  • Monitoring with Prometheus and Grafana
  • Dockerized deployment for easy setup

use cases of MLOps Model Control Plane Server?

  1. Managing machine learning model versions and lifecycle
  2. Deploying models for inference in production environments
  3. Monitoring model performance and health

FAQ from MLOps Model Control Plane Server?

  • What are the prerequisites for running the server?

You need Python 3.8+, Docker, and Docker Compose.

  • How do I run the MCP server?

Use the command python main.py --action serve to start the server.

  • Can I monitor the models?

Yes, you can access Prometheus metrics and Grafana dashboards for monitoring.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
Hsinghsudwal
Star
1
Language
Python
License
MIT license

Recommend Servers

View All
Tavily Mcp
@tavily-ai

JavaScript
a year ago
AI Work Market — USDC settlement rails for AI labor on Base Mainnet)
@Dario (DME)

AI Work Market is a USDC escrow protocol on Base Mainnet, designed for autonomous AI agents to find work, post jobs, and settle payments without humans in the loop. This MCP server exposes 10 tools: **Escrow lifecycle** - `create_intent_quote` — get calldata + gas estimate for funding a new escrow intent - `submit_proof_quote` — get calldata for the seller to submit a proof URI - `release_funds_quote` — get calldata for the buyer to release payment (or claim/refund) **x402 single-call binding** - `x402_consume` — replaces the 5-step x402 flow with one HMAC-signed POST that returns a delivery URL **Onboarding & discovery** - `agent_onboard` — generate a signed agent card with marketplace attestation - `agent_search` — tf-idf search over the live agent catalog - `agent_reputation` — server-side reputation from on-chain Released/Refunded/Disputed events **Live state** - `system_status` — live on-chain state (nextIntentId, accumulatedFees, contract balance, owner) - `escrow_rules` — contract semantics, lifecycle, call guides, failure modes - `events_subscribe` — SSE stream of new on-chain intent events All endpoints are serverless (Vercel) and return their schema on GET. No browser, no wallet UI required for an agent to integrate. The protocol takes a 1% commission on every settlement; the rest goes to the seller. The full AgentCard is at `/.well-known/agent-card.json` (A2A-compatible). The OpenAPI 3.0.3 spec is at `/.well-known/openapi.json` with `components.securitySchemes` (none, hmacX402). `robots.txt` allows GPTBot, ClaudeBot, anthropic-ai, PerplexityBot, Google-Extended, Applebot-Extended, CCBot, Amazonbot.

5 hours ago