Cloud Machine Learning Platforms and Infrastructure Questions

Knowledge of cloud hosted machine learning and artificial intelligence platforms and the supporting infrastructure used to develop, train, deploy, and operate models at scale. Candidates should be familiar with major managed offerings such as Amazon SageMaker, Google Cloud artificial intelligence platform, and Microsoft Azure Machine Learning and understand capabilities including pretrained models, managed training jobs, managed inference endpoints, model registries, and managed pipelines. Key areas include differences between cloud and local training, distributed and hardware accelerated training options, cost trade offs including spot and preemptible instances, serving patterns such as serverless inference, hosted endpoints and batch processing, autoscaling strategies for inference, model versioning and rollout strategies including canary and blue green deployments, integration with data storage, feature stores and data pipelines, and model monitoring, logging and drift detection. Candidates should also be able to explain when to use managed services versus self hosted or on premises solutions, discussing trade offs around productivity, operational overhead, control and customization, vendor lock in, security, data residency and compliance, as well as operational practices such as continuous integration and deployment for models, testing and validation in production, observability and cost optimization.