Design model deployment, monitoring, and low-latency inference

Q: Design model deployment, monitoring, and low-latency inference

This question evaluates competency in ML system design and production engineering—covering model deployment and versioning, safe rollouts and rollbacks, monitoring of service health, data quality/drift, model performance and business metrics, and latency optimization for low-latency inference.

Q: 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.

Question

You have trained a fraud detection model and need to productionize it.

Part A: Deployment

How would you deploy an ML model to production?
What artifacts do you version and how do you enable safe rollouts/rollbacks?

Part B: Monitoring

After deployment, how do you monitor the model?
What metrics do you track for:
- service health,
- data quality/drift,
- model performance,
- business impact?

Part C: Latency SLO

The model is deployed behind an online API, but it is missing a strict latency requirement: p99 latency < 50 ms.

How do you diagnose where time is spent?
What concrete changes would you consider across features, model, infrastructure, and serving to meet the SLO without unacceptable accuracy loss?

Design model deployment, monitoring, and low-latency inference

Part A: Deployment

Part B: Monitoring

Part C: Latency SLO

Solution

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Design model deployment, monitoring, and low-latency inference

Overview

Part A: Deployment

Part B: Monitoring

Part C: Latency SLO

Solution

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