AI / ML Infrastructure

Model serving is the infrastructure that takes a trained ML model and exposes it as a low-latency, high-throughput prediction endpoint. At scale, serving must handle batching, GPU memory management, model versioning, and graceful rollouts -- problems that look nothing like training.

A feature store is a centralized platform for defining, storing, and serving ML features consistently across training and inference. It solves the train-serve skew problem by ensuring the exact same feature transformation logic produces data for both offline model training and online prediction.

ML training pipelines orchestrate the end-to-end workflow from raw data to a validated model artifact: data ingestion, preprocessing, feature engineering, distributed training, hyperparameter tuning, evaluation, and model registration. Reproducibility, idempotency, and efficient GPU utilization are the key engineering challenges.

GPU orchestration manages the scheduling, allocation, and lifecycle of GPU resources across training and inference workloads. Unlike CPU workloads, GPUs are expensive ($2-$30/hour per GPU), scarce, and require topology-aware scheduling to maximize interconnect bandwidth between co-located GPUs.

An ML model registry is a centralized store for versioned model artifacts, metadata, and lifecycle state. It is the 'source of truth' that connects training pipelines to serving infrastructure, enabling reproducibility, auditability, and governance across the model lifecycle from experiment to production to retirement.

A/B testing (online controlled experimentation) is the gold standard for measuring the causal impact of ML model changes on business metrics. It splits live traffic between a control (current model) and treatment (new model) to measure statistically significant differences in user behavior, revenue, or engagement.

Retrieval-Augmented Generation (RAG) grounds LLM responses in external knowledge by retrieving relevant documents at query time and including them in the prompt context. It combines the fluency of generative models with the accuracy and recency of a searchable knowledge base, without the cost and latency of fine-tuning.