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About this interview
A technical interview on Deployment Strategies, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Contrast batch inference, real-time REST serving, and streaming inference on their latency, throughput, and cost profiles
Explain how a model is packaged for each deployment mode and what containerisation contributes
Discuss the trade-offs between synchronous (request-response) and asynchronous (queue-based) serving architectures
Describe how to choose between deployment modes given SLA requirements and feature freshness needs
Topics covered
Deployment modes overviewBatch inferenceReal-time REST servingBatch schedulingLatency and throughput tradeoffsContainerisationStreaming inferenceSynchronous vs asynchronous servingMode selectionFeature freshnessModel loading in batchReal-time serving operationsTraining-serving skewCost profiles
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“Can you walk me through the three main ways you can deploy a trained machine learning model in production — batch, real-time REST, and streaming — and give me a rough sense of what distinguishes each one?
“What does feature freshness mean in the context of model deployment, and why does it matter when choosing between batch and real-time serving?
“What are the tradeoffs between deploying a model on a managed serving platform like SageMaker Endpoints versus running it on your own Kubernetes cluster with something like KServe?