Mlops Guides
Explore 5 curated mlops guides for building with AI coding agents — what each one does, when to use it, and how to add it to your workflow.
Deploying LLMs to Production: A Reliability & Cost Checklist
Take an LLM feature from prototype to production: API vs self-host, provider fallback, retries, caching, observability, eval gates, and safe rollout.
Preparing a Fine-Tuning Dataset: Cleaning, Synthetic Data, and Eval Splits
The dataset is the model. How to build a fine-tuning dataset that works — format, curation, cleaning, synthetic augmentation, and a leak-free eval split.
Fine-Tune vs RAG vs Prompt vs Distill: The 2026 Decision Tree
When to reach for prompt engineering, RAG, fine-tuning, or distillation — what each actually changes, where each fails, and how to combine them.
LLM Observability in Production: Traces, Evals, Cost, and Feedback
Instrument production LLM applications with end-to-end traces, online evals, cost and latency metrics, privacy controls, alerts, and a failure-feedback loop.
Self-Host vs API: When Does Running Your Own LLM Actually Pay Off?
The real economics of self-hosting an LLM vs. calling a hosted API — GPU utilization, privacy, latency, and the hidden ops costs that decide the crossover.