The Model Integration Lifecycle (MILC) can be viewed as a practical extension—or counterpart—to the Model Infrastructure Lifecycle. Its purpose is to define common terminology and provide a structured framework for teams adopting large language models (LLMs) and managing their transition into and out of production environments.

MILC aligns closely with the Model Development Lifecycle (MDLC), while also embracing DevOps and MLOps principles of continuous improvement and evaluation.

PhasePurposeExample
Requirements GatheringIdentify business needs and model fitNeed a model to summarize tickets in less than 3s with PII redaction
Feasibility AnalysisEvaluate performance, latency, cost, infra readinessLLaMA 2-70B meets quality targets; latency less than 1s possible via serverless CentML endpoint
Design & ArchitecturePlan API integration, security, auth, observabilityUse CentML’s /v1/chat/completions endpoint (OpenAI-compatible); authenticate using API tokens; log request/response metadata to BigQuery
Development & IntegrationBuild prompt templates, format inputs/outputs, handle tokens, retriesBuild API route to send prompt to CentML endpoint and return structured response
Fine-tuning (optional)Improve model behavior on domain-specific tasksFine-tune LLaMA 2 on internal support ticket dataset
Testing & ValidationRun unit, functional, latency, and accuracy testsCompare LLM summaries to human-written ones; use ROUGE/LFQA scoring
A/B Testing or Canary DeployGradually release the model to validate behavior and avoid regressionsRoute 10% of support queries to new model or prompt version, measure impact
DeploymentRoll out model integration in productionDeploy autoscaled API backend with load-balanced access to CentML endpoint
Monitoring & OptimizationTrack usage, quality, token cost, driftMonitor latency, output quality; alert on spike in cost per token
Model Retirement / ReplacementRetire underperforming models or roll in upgraded versionsDecommission v1 endpoint after v2 adoption; archive prompts and logs for compliance

Where Does CentML Fit In?

CentML can support teams during multiple phases of the Model Integration Lifecycle. See below for details

Model Integration Life Cycle Phases and How CentML Supports Them

  1. Requirements Gathering: Teams can evaluate CentML’s API features (e.g., latency, scalability, cost models) as well as model quality to inform LLM feasibility.
  2. Feasibility Analysis: CentML allows instant access to high-performance LLM endpoints, enabling latency, throughput, and cost testing early on.
  3. Design & Architecture: CentML exposes standardized /v1/chat/completions endpoints with token-based auth, simplifying architecture planning.
  4. Development & Integration: Developers integrate CentML endpoints using standard OpenAI SDKs or HTTP clients like httpx, minimizing boilerplate.
  5. Fine-tuning (optional iteration step): While CentML currently focuses on serving, Custom Model Endpoints and LLM serving support serving fine-tuned models.
  6. Testing & Validation: Teams can test prompt formats and model behavior in CentML’s hosted environment with minimal infrastructure overhead.
  7. A/B Testing or Canary: Deploy CentML’s flexibility allows parallel deployments. Users must implement endpoint routing logic for A/B or canary testing via external tooling.
  8. Deployment: CentML handles scalable, production-ready deployment — no need to manage GPU infra or custom serving stacks.
  9. Monitoring & Optimization: Users can track token usage, latency, and costs through CentML’s reporting, and tune prompt performance accordingly.
  10. Model Retirement / Replacement: Teams can switch CentML endpoints to newer models they’ve evauluated with CentMLs serverless endpoints or deploy multiple endpoints with different model versions without re-architecting infrastructure.

Want to Learn More?

Feel free to reach out to sales@centml.ai to engage with the solutions team, check out the examples codex, read The CentML Blog, and visit CentML’s website

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