Day 23: Scaling & Managing LLMs in Production Environments
đź“š Key Learnings
What makes LLMs different from traditional ML models
Fine-tuning vs prompt engineering
Deployment patterns for LLMs (real-time vs batch, on-demand vs always-on)
Managing GPU infrastructure and resource scheduling
Logging, monito...
bittublog.hashnode.dev18 min read
CapeStart
AI, XAI, NLP, DL, ML, GenAI
One thing I’d add is that most teams underestimate how hard observability becomes once they move past the “single GPU demo” phase. Logging token counts, caching hits, and model-version drift saves way more money and headaches than people expect. Scaling LLMs isn’t just about bigger GPUs, it’s about tighter feedback loops.