How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

What difficulty level is this interview question?

This is a hard difficulty ML System Design question, commonly asked during Onsite rounds at Anthropic.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Anthropic during technical interviews.

Design a low-latency ML inference API | Anthropic Interview Question

Quick Overview

This question evaluates competency in ML system design for real-time, low-latency inference APIs, including multitenancy, SLO/SLI definition, feature retrieval, model serving and rollout strategies, observability, cost control, and security/compliance within the ML System Design category.

System Design: Low‑Latency ML Inference API (Real‑Time)

Context

You are designing an in‑region, synchronous inference API used by product surfaces (e.g., ranking, fraud checks, personalization) that require tight latency and high availability. The service must support multiple tenants, safe model rollouts, and strong observability, while controlling cost.

Requirements

Target SLOs

Propose p50/p95 (and optionally p99) end‑to‑end latency targets and availability targets.
Define SLIs and error budgets.

Define request/response schema, including idempotency, model/version selection, and metadata for traceability.
Authentication and authorization approach.
Rate limiting and quotas.
Multitenancy (tenant isolation, quotas, and model routing).

Architecture

Load balancing and edge protections.
Stateless API tier design.
Feature retrieval (online store), consistency, and TTLs.
Model serving choices (CPU/GPU), dynamic batching, quantization, caching.
Autoscaling strategies for API, feature store, and model servers.

Release Safety and Experimentation

Model versioning and registry.
Canary/shadow, rollback criteria.
Online A/B (assignment, metrics, guardrails).

Observability and Quality

Metrics, logs, tracing (end‑to‑end and per stage).
Data/feature quality checks and drift detection.

Cost and Reliability

Cost controls (utilization targets, right‑sizing, caching, tiering).
Fallback behavior under partial outages or capacity shortfalls.

Security and Compliance

Request security, mTLS, secrets management.
PII handling, retention, and auditability.
Regionalization/data‑sovereignty and disaster recovery plan.

Deliverables

A concrete proposal covering the above, including clearly stated numerical targets and trade‑offs.
Any assumptions you make that influence the design.

Quick Overview

Requirements

Target SLOs

Propose p50/p95 (and optionally p99) end‑to‑end latency targets and availability targets.

Define SLIs and error budgets.

API

Define request/response schema, including idempotency, model/version selection, and metadata for traceability.

Authentication and authorization approach.

Rate limiting and quotas.

Multitenancy (tenant isolation, quotas, and model routing).

Architecture

Load balancing and edge protections.

Stateless API tier design.

Feature retrieval (online store), consistency, and TTLs.

Model serving choices (CPU/GPU), dynamic batching, quantization, caching.

Autoscaling strategies for API, feature store, and model servers.

Release Safety and Experimentation

Model versioning and registry.

Canary/shadow, rollback criteria.

Online A/B (assignment, metrics, guardrails).

Observability and Quality

Metrics, logs, tracing (end‑to‑end and per stage).

Data/feature quality checks and drift detection.

Cost and Reliability

Cost controls (utilization targets, right‑sizing, caching, tiering).

Fallback behavior under partial outages or capacity shortfalls.

Security and Compliance

Request security, mTLS, secrets management.

PII handling, retention, and auditability.

Regionalization/data‑sovereignty and disaster recovery plan.

Design a low-latency ML inference API

Quick Overview

System Design: Low‑Latency ML Inference API (Real‑Time)

Context

Requirements

Deliverables

Solution

Comments (0)

Design a low-latency ML inference API

Quick Overview

System Design: Low‑Latency ML Inference API (Real‑Time)

Context

Requirements

Deliverables

Solution

Comments (0)