The Never-Ending Journey
Deploying your model isn't the end — it's the beginning. Production LLMs require continuous monitoring, feedback collection, and periodic updates to stay relevant and safe.
Like Maintaining a Garden
A garden doesn't tend itself. You must water it (respond to feedback), prune dead branches (fix errors), plant new seeds (add capabilities), and watch for pests (safety issues). LLMs need the same ongoing care.
24/7
Monitoring
Daily
Feedback Review
Weekly
Safety Audits
Monthly
Model Updates
Key Metrics to Monitor
⏱️
Latency (P50/P99)
Response time at 50th/99th percentile
📈
Throughput
Requests per second, tokens per second
❌
Error Rate
Failed requests, timeouts, 5xx errors
👍
User Satisfaction
Thumbs up/down, regeneration rate
🛡️
Safety Violations
Harmful outputs, refusal rate
💰
Cost per Query
Compute, API calls, storage
Drift Detection
Models can degrade over time as the world changes. News events, new slang, and evolving user needs can make your model less relevant.
Data Drift
Input distribution shifts from training data
Concept Drift
Meaning of concepts changes over time
Performance Drift
Quality degrades on new use cases
MLOps Best Practices
🔄 Version Control
Track model versions, data versions, and config changes. Enable rollback if issues arise.
🧪 A/B Testing
Test new models on subset of traffic before full rollout. Compare metrics head-to-head.
📝 Feedback Loops
Collect user feedback (ratings, edits, regenerations). Use for continuous fine-tuning.
🚨 Incident Response
Have playbooks for safety incidents. Enable quick model rollback or output filtering.
✅
Congratulations! 🎉
You've completed the entire LLM lifecycle journey — from Day 0 research to production MLOps!
- 16 stages across 8 phases
- From data collection to continuous learning
- Interactive simulations and technical deep-dives
- Ready to build your own LLM!