Model Lifecycle

Published 2026-04-20·Updated 2026-06-26·1 min read

AI documentation guide

Overview

Production models do not end with a single deployment. This section covers all stages of the model lifecycle, from custom model deployment and LoRA fine-tuning pipelines to MLOps orchestration (Kubeflow/Argo) and Continuous Training (GRPO/DPO/Canary rollout).

Documents

Model Deployment

Hands-on guide to deploying large open-source models on EKS, based on the GLM-5.1 experience

Custom Model Pipeline

Building a domain-optimized model serving pipeline with LoRA Fine-tuning, Multi-LoRA Hot-swap, and SLM Cascade Routing

MLOps Pipeline

End-to-end ML lifecycle management with Kubeflow + MLflow + vLLM + ArgoCD GitOps

Open-Weight Model Automation Pipeline

An architecture that automates open-weight model onboarding through a seven-stage pipeline, from HuggingFace leaderboard scanning and benchmark reproduction to instance performance profiling, deployment guide generation for multiple targets, and global Spot capacity acquisition, with human involvement at approval gates

Continuous Training

3 items