// TOPIC
#mlops
2 articles
◆◆◆AdvancedOpenAIMeta
01Design an LLM Fine-Tuning Platform
Turn a base model and a dataset into a deployed fine-tuned adapter at scale — the end-to-end platform covering dataset ingestion, LoRA/QLoRA/DPO training, fault-tolerant distributed GPU scheduling, eval gating, and multi-LoRA serving for hundreds of concurrent fine-tunes.
#interview#ai#llm
37 min◆◆◆AdvancedUberAirbnb
02Design a Feature Store
Serve the exact same feature values to model training and online inference — eliminating training-serving skew — across batch, streaming, and on-demand tiers at sub-10ms latency and millions of reads per second. The architecture powering Uber Michelangelo, Airbnb Chronon, and DoorDash Gigascale.
#interview#ai#mlops
26 min