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Migrate a cloud agent to local

Migrating to local isn't just changing a URL and calling it done. Latency, context, tool calling, quality, observability, and hardware limits all change. Do it in stages and keep an escape hatch.

  • Inventory cloud dependencies before migrating.
  • Test compatibility of prompts, tools, and context.
  • Decide what stays local and what keeps an external fallback.
Terminal
migration_matrix:
  tasks:
    classify_email:
      target: "local"
      model: "ollama/qwen2.5:7b"
      risk: "low"
    draft_legal_reply:
      target: "hybrid"
      local_first: true
      cloud_requires_approval: true
    edit_codebase:
      target: "local_with_review"
      requires_tests: true

Tests before changing production

  • Same input against cloud and local.
  • Compare quality, latency, and format.
  • Test minimal tool calling.
  • Measure real and truncated context.
  • Record failures that require fallback.

Official sources

  • LiteLLM routing and load balancing
  • LiteLLM reliability and fallbacks
  • Ollama API
  • vLLM OpenAI-compatible server
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How all courses connect

This is not a checklist. Start with the foundation, choose an outcome, and go deeper only when your project needs more control.

  1. 1Understand
  2. 2Apply or build
  3. 3Operate with confidence
01

Choose an application

Turn the foundation into a visible outcome: a website, a business improvement, media, or an interactive experience.

Continue into the technical branch when you need to maintain code, data, or infrastructure.

02

Build with code

Prepare your environment, work with coding agents, and run models while keeping control of your projects.

This branch prepares you to design and operate reliable AI systems.

03

Take systems to production

Combine retrieval, agents, evaluation, security, deployment, and model adaptation when the problem requires it.

You do not need every course: choose the component your system needs and return as it grows.

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