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Clipping · Exhibit · Agentic MLOps Tradeoff Engine

Pasted from the desk

Veridian

Veridian is a multi-agent MLOps tradeoff engine that autonomously intercepts Terraform and Kubernetes infrastructure-as-code changes, analyzes them for sustainability and cost efficiency, and recommends optimized alternatives — all without human intervention.

Halftone photograph: an engineer at a drafting table, an infrastructure drawing unrolled before him.
Veridian intercepts the change — file photo.
12344a5678Fig.1.Fig.2.the throttle, sectioned

Reference

  1. 1.HOPPER — the intercept — Terraform / K8s
  2. 2.TELEGRAPH — GitLab Duo + MCP
  3. 3.CRUCIBLE — Vertex AI — profile the model
  4. 4.VALVE — quantized, low-carbon hardware
  5. 5.BEDPLATE — Cloud Run
  6. 6.LEDGER — BigQuery — not this request
  7. 7.FUNNEL — Python — glue, not this path
  8. 8.GAUGE — 99.9% uptime

APPARATUS FOR AN ECONOMIZED PLANT. Filed Apr. 2026.

12344a5678Fig.1.Fig.2.the throttle, sectioned

Tech

  • GitLab Duo
  • GCP Vertex AI
  • BigQuery
  • Python
  • MCP
  • Terraform

The line

  1. 01

    Architected a multi-agent orchestration pipeline using GitLab Duo and Model Context Protocol (MCP) to autonomously intercept and optimize infrastructure-as-code in real-time.

  2. 02

    Engineered a secure MCP server on Cloud Run that queries GCP Vertex AI to dynamically profile ML models, recommending quantized, low-carbon hardware equivalents.

  3. 03

    Implemented an immutable ESG ledger using GCP BigQuery to track carbon savings and compute tradeoffs for enterprise ML deployments.

  4. 04

    Achieved a 99.9% uptime for the deployment infrastructure, cutting cloud-associated emissions by an additional 15% through preemptive resource scheduling.

Measurable impact

Zero-touch ML remediation

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