ML
ML Interview Roadmap
← Study plan·MLOps

Day 63 of 133

Serving infrastructure: Triton / TorchServe / BentoML / k8s + DSA 1-D DP

Dynamic batching, KServe, autoscaling, safe rollback.

DSA · NeetCode 1-D DP

  • Decode WaysDSA · 1-D DP

    Interview questions to prep

    1. State the DP: define the state, the transition, and the base case explicitly.
    2. Top-down (memoized recursion) vs bottom-up (tabulation) — which is more natural here, and why?
    3. Can you space-optimize from O(n) to O(1)? Show the rolling-window trick.
  • Coin ChangeDSA · 1-D DP

    Interview questions to prep

    1. State the DP and the order of fills. Why does coin-first-then-amount give you each combination?
    2. How does coin-change-ii (number of ways) differ from coin-change-i (min coins)?

MLOps · Serving infrastructure

  • Model servers: TorchServe, Triton, BentoML, SeldonMLOpsTriton

    Interview questions to prep

    1. Compare TorchServe, Triton, and BentoML — when does each fit?
    2. What is dynamic batching and why does it matter for GPU utilization?
  • Containerization & Kubernetes for MLMLOpsKubeflow

    Interview questions to prep

    1. Walk through deploying a model on Kubernetes with autoscaling.
    2. When do you reach for KServe vs custom Deployment + HPA?
  • Traffic routing, model rollback, A/B servingMLOpsKServe

    Interview questions to prep

    1. How would you do a safe rollback when a new model regresses online metrics?
    2. What guardrail metrics would automatically trigger a rollback without a human in the loop?

References & further reading