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Interactive 3D/LLMOps Pipeline
LLMOps Lifecycle Pipeline
Mode: Iterative - 6 active stages
Active Stages
🗂
Stage 1: Dataset Curation
Collect, clean, and filter training data
Healthy▼
↓
🎯
Stage 2: Fine-Tuning / RLHF
SFT → reward model → PPO or DPO alignment
Healthy▼
↓
📊
Stage 3: Evaluation
Automated evals + human red-teaming
Warning▼
↓
🚀
Stage 4: Deployment
Serve via vLLM, TGI, or custom inference
Healthy▼
↓
📡
Stage 5: Monitoring
Track latency, quality drift, cost per token
Critical▼
↓
🔄
Stage 6: Retraining Trigger
Detect drift → kick off new training run
Warning▼
Deployment Strategy: Canary Deployment
Route 5% → 20% → 100% of traffic gradually. Monitor error rate and latency at each step.
RISK
Low
ROLLBACK
< 2 min
Pipeline Mode
Deployment Strategy
Health Legend
Healthy
Warning
Critical
Summary
6 active stages
3 healthy
2 warning
1 critical
Click any stage card to expand engineering details.

LLMOps Pipeline - Interactive Visualization

LLMOps extends MLOps practices to large language models, covering the full lifecycle from dataset curation and fine-tuning through evaluation, deployment, and continuous monitoring. Understanding retraining triggers - when model quality degrades or data distribution shifts - is critical for keeping LLMs production-ready. This interactive visualization walks through each stage of the LLMOps pipeline and shows how they connect.

  • Dataset curation: see how raw data is filtered, deduplicated, and formatted for fine-tuning
  • Fine-tuning: visualize training runs with loss curves and hyperparameter choices
  • Evaluation: automated evals gate deployment - compare baseline vs fine-tuned model scores
  • Monitoring: track real-time quality metrics and detect when model performance degrades

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.