Talked through a few projects where I used SageMaker and S3, maybe leaned too hard on the infrastructure side and not enough on the actual modeling decisions.
Structure your answer around a specific ML project, highlighting the AWS services used at each stage of the ML lifecycle. Emphasize the trade-offs and reasons for choosing those services, and connect them to business impact like scalability, cost, or latency.
Pro tip: Focus on why you chose specific AWS services over alternatives, and quantify the impact (e.g., reduced training time by 40%, cut costs by 30%). This shows you understand trade-offs, not just tools.
Briefly describe the ML project, its goal, and the scale (data size, model complexity, latency requirements). This helps the interviewer understand the constraints that drove your AWS choices.
Walk through the stages: data ingestion (e.g., Kinesis, S3), preprocessing (e.g., Glue, EMR), training (e.g., SageMaker, EC2), deployment (e.g., SageMaker endpoints, Lambda), and monitoring (e.g., CloudWatch). Explain which services you used and why.
Discuss key decisions: why you chose a particular service over alternatives (e.g., SageMaker vs. custom EC2), how you optimized cost/performance, and any challenges you faced and resolved.
Share measurable outcomes: improved model accuracy, reduced training time, lower inference latency, cost savings, or scalability improvements. This demonstrates business value.
Summarize lessons learned and how you would apply them to DoorDash's ML challenges, such as real-time delivery prediction or personalization at scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.