Self-Hosted Models for Sensitive Environments training course

Build versus buy analysis: total cost of ownership, data control, latency, and capability gaps between hosted and self-hosted options

Model selection workshop: Llama, Mistral, Phi, and others compared on benchmark scores and hardware requirements for your infrastructure

Ollama and vLLM install lab: getting a chosen model running on target hardware with a working endpoint in under one hour

Quantisation explained and applied: GGUF formats, Q4 versus Q8 tradeoffs, and live measurement of quality impact on your test prompts

Local API setup: configuring an OpenAI-compatible endpoint so existing application code requires minimal or no changes

Network security lab: binding to internal interfaces only, firewall rule configuration, and role-based access control setup

Resource monitoring setup: dashboards tracking GPU utilisation, memory pressure, and request throughput with alert thresholds

Model update process: pulling new versions safely, running quality tests before promoting, and a documented rollback procedure

Quality benchmarking: running your evaluation test set against the self-hosted model and a cloud baseline side by side

Handover documentation: writing a runbook covering deployment steps, monitoring response, update procedure, and incident handling