AI and Machine Learning Infrastructure Questions

Design infrastructure to train, validate, and serve machine learning models at scale. Topics include selecting instance types with appropriate graphics processing units, cluster and distributed training architectures, data pipelines and feature engineering storage, model versioning and registry patterns, real time inference and batch scoring architectures, autoscaling considerations for latency and throughput targets, and cost optimization techniques for compute heavy workloads. Cover managed platform options such as Azure Machine Learning and cognitive services, retrieval augmented generation patterns using vector databases, online and offline feature stores, monitoring model performance and data drift, and machine learning operations practices for continuous training, deployment pipelines, testing, and governance.