This is a big one and I didn't pace myself well.
Start by clarifying requirements (latency, throughput, model types, multi-tenancy) and then walk through the design holistically, covering API contract, routing, batching, model lifecycle, and GPU sharing. Emphasize trade-offs and justify decisions based on HubSpot's likely needs (e.g., CRM predictions, real-time scoring).
Pro tip: Show awareness of GPU memory constraints and propose a strategy like dynamic batching with a timeout to balance latency and throughput, and mention using NVIDIA MPS or MIG for multi-model isolation. Also, highlight the importance of observability and graceful degradation.
Ask about expected QPS, latency SLOs, model sizes, and whether requests are real-time or batch. This shapes decisions on sync vs async, batching, and GPU sharing.
Define request/response schema (e.g., JSON with model ID, inputs, parameters) and versioning strategy (e.g., URL versioning or header-based). Consider sync for low-latency and async for long-running or batch requests.
Route requests to appropriate model instances based on model ID and version. Use a load balancer with health checks and possibly a queue for async requests.
Use dynamic batching with a max batch size and timeout to optimize GPU utilization. Handle model loading/unloading with a cache and pre-warming to reduce cold starts.
Discuss strategies like time-slicing, MPS, or MIG to share GPUs among models. Consider memory management, isolation, and prioritization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about scaling on GPU utilization metrics rather than CPU/memory since that's the actual bottleneck.
Start by clarifying the workload characteristics and GPU heterogeneity, then propose a multi-layered autoscaling strategy that combines cluster-level and pod-level scaling. Describe a placement strategy that uses node affinity, taints/tolerations, and a custom scheduler or service mesh to route requests based on GPU capabilities and real-time load.
Pro tip: Emphasize the importance of defining clear SLAs and using metrics like GPU utilization, memory, and queue depth to drive scaling decisions, rather than just CPU. Also, mention the trade-off between bin-packing for cost efficiency and spreading for fault tolerance.
Ask about the types of GPUs, workload patterns (batch vs. real-time), latency SLAs, and cost constraints to tailor the solution.
Propose a hierarchical autoscaling approach: cluster autoscaler for node provisioning and horizontal pod autoscaler (HPA) with custom metrics for pod scaling, considering GPU-specific metrics.
Use node labels and affinity to match GPU types to workloads, and implement a routing layer (e.g., Istio, custom load balancer) that considers GPU availability and request requirements.
Discuss trade-offs between cost, performance, and reliability, and how to handle node failures, GPU memory fragmentation, and cold starts.
Recap the approach, highlighting how it meets the requirements, and suggest monitoring and iterative improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's architecture and SLOs, then propose a layered observability stack covering latency, throughput, and GPU metrics. Emphasize actionable metrics, alerting, and how they tie to user experience and business outcomes.
Pro tip: Tie every metric to an SLO and a potential action—interviewers love candidates who think about alert fatigue and mean time to resolution, not just dashboards.
Ask about the system's components, expected traffic, and latency/throughput targets. Define SLOs for latency (e.g., p99 < 200ms) and throughput (e.g., 10k RPS).
Propose measuring latency at multiple layers: client-side, API gateway, service, and GPU inference. Use histograms and percentiles (p50, p95, p99) to capture tail latency.
Track requests per second, queue depth, batch sizes, and error rates. Monitor saturation points and autoscaling triggers.
Collect GPU utilization, memory usage, temperature, power draw, and SM occupancy. Use NVIDIA DCGM or similar tools, and correlate with latency/throughput.
Define alerts based on SLO violations (e.g., error budget burn) and create dashboards for real-time monitoring and post-mortems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Token bucket per tenant at the API gateway level, with a separate queue per tenant downstream so one noisy tenant can't starve others.
Start by clarifying the platform's requirements—expected QPS, tenant count, and isolation guarantees—then propose a layered architecture: rate limiting at the API gateway and per-tenant quotas, with tenant context propagated through the inference pipeline. Discuss trade-offs between centralized vs. distributed rate limiting and between hard vs. soft isolation, and explain how you'd handle fairness and noisy-neighbor problems.
Pro tip: Tie your answer to HubSpot's multi-tenant SaaS context by emphasizing observability and per-tenant metrics, and mention that you'd start with a simple token bucket per tenant before scaling to a distributed solution like Redis with sliding windows.
Ask about expected traffic patterns, number of tenants, latency budgets, and whether isolation is for performance, security, or both. This shows you avoid over-engineering and align with business needs.
Propose a multi-level approach: global rate limits at the edge, per-tenant quotas, and per-endpoint limits. Compare algorithms like token bucket, leaky bucket, and sliding window, and choose based on burst tolerance and accuracy.
For a distributed system, use a centralized store like Redis with atomic operations (e.g., Lua scripts) or a decentralized approach with consistent hashing. Discuss trade-offs: Redis adds latency and a single point of failure, while decentralized may have consistency issues.
Isolate tenants at multiple layers: separate API keys, per-tenant resource quotas (CPU/GPU/memory), and data partitioning. Consider soft isolation (logical) vs. hard isolation (dedicated instances) based on cost and security requirements.
Instrument per-tenant metrics (latency, error rates, quota usage) and set up alerts. Test with load simulations to ensure fairness and no noisy neighbors, and be ready to adjust limits dynamically.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Retries with exponential backoff, but only for idempotent inference requests.
Start by framing failure handling as a layered strategy: first, ensure resilience at the request level with retries and timeouts; second, manage risk when deploying new model versions using canary or A/B rollouts with clear metrics and rollback plans. Emphasize trade-offs between reliability, latency, and cost, and tie your answer to HubSpot's scale and customer-centric culture.
Pro tip: Mention that retries should be idempotent and use exponential backoff with jitter to avoid thundering herd, and that canary rollouts should be gated by business metrics (e.g., conversion) not just technical ones (e.g., latency).
Ask about the system's SLAs, traffic volume, and criticality of the model predictions. Identify whether the model is user-facing or internal, and what failure modes are acceptable.
Define retry policies (e.g., exponential backoff with jitter, max attempts), timeouts (per attempt and overall), and fallback strategies (e.g., cached predictions, default model, or graceful degradation).
Use canary releases to route a small percentage of traffic to the new model, monitor key metrics (latency, error rate, business KPIs), and gradually increase traffic if healthy. For A/B tests, define hypotheses, control/treatment groups, and statistical significance.
Set up automated alerts for anomalies, and define rollback triggers (e.g., error rate > 1%, latency > 200ms). Ensure rollback is fast and doesn't require manual intervention.
After each rollout, conduct a post-mortem to refine thresholds, retry policies, and rollout strategies. Use feedback to improve future deployments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.