An Introduction to the Model Integration Lifecycle
Phase | Purpose | Example |
---|---|---|
Requirements Gathering | Identify business needs and model fit | Need a model to summarize tickets in less than 3s with PII redaction |
Feasibility Analysis | Evaluate performance, latency, cost, infra readiness | LLaMA 2-70B meets quality targets; latency less than 1s possible via serverless CentML endpoint |
Design & Architecture | Plan API integration, security, auth, observability | Use CentML’s /v1/chat/completions endpoint (OpenAI-compatible); authenticate using API tokens; log request/response metadata to BigQuery |
Development & Integration | Build prompt templates, format inputs/outputs, handle tokens, retries | Build API route to send prompt to CentML endpoint and return structured response |
Fine-tuning (optional) | Improve model behavior on domain-specific tasks | Fine-tune LLaMA 2 on internal support ticket dataset |
Testing & Validation | Run unit, functional, latency, and accuracy tests | Compare LLM summaries to human-written ones; use ROUGE/LFQA scoring |
A/B Testing or Canary Deploy | Gradually release the model to validate behavior and avoid regressions | Route 10% of support queries to new model or prompt version, measure impact |
Deployment | Roll out model integration in production | Deploy autoscaled API backend with load-balanced access to CentML endpoint |
Monitoring & Optimization | Track usage, quality, token cost, drift | Monitor latency, output quality; alert on spike in cost per token |
Model Retirement / Replacement | Retire underperforming models or roll in upgraded versions | Decommission v1 endpoint after v2 adoption; archive prompts and logs for compliance |