What is Load Testing for ML?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Record production traffic patterns for 2-4 weeks to capture daily and weekly cycles. Use tools like Locust or k6 to replay these patterns at 1.5-2x normal volume. Include request payload variety since different input sizes affect inference time differently. Test both sustained load and spike scenarios. A common mistake is testing with uniform requests, which misses the latency variance from different input complexities.

For real-time predictions, aim for p50 under 50ms and p99 under 200ms. Batch inference is more flexible but should complete within SLA windows. The key metric is tail latency (p95/p99), not average, because users experience the worst cases. E-commerce recommendation models typically need sub-100ms; fraud detection needs sub-50ms. Set SLOs based on business impact, not technical convenience.

Run load tests after every model update, infrastructure change, or traffic pattern shift. At minimum, run monthly even without changes, as dependency updates and platform changes can silently affect performance. Automate load tests in your CI/CD pipeline for model deployments. Teams that skip regular load testing are often surprised by holiday traffic spikes or marketing campaign surges that overwhelm their serving infrastructure.

Record production traffic patterns for 2-4 weeks to capture daily and weekly cycles. Use tools like Locust or k6 to replay these patterns at 1.5-2x normal volume. Include request payload variety since different input sizes affect inference time differently. Test both sustained load and spike scenarios. A common mistake is testing with uniform requests, which misses the latency variance from different input complexities.

For real-time predictions, aim for p50 under 50ms and p99 under 200ms. Batch inference is more flexible but should complete within SLA windows. The key metric is tail latency (p95/p99), not average, because users experience the worst cases. E-commerce recommendation models typically need sub-100ms; fraud detection needs sub-50ms. Set SLOs based on business impact, not technical convenience.

Run load tests after every model update, infrastructure change, or traffic pattern shift. At minimum, run monthly even without changes, as dependency updates and platform changes can silently affect performance. Automate load tests in your CI/CD pipeline for model deployments. Teams that skip regular load testing are often surprised by holiday traffic spikes or marketing campaign surges that overwhelm their serving infrastructure.

Record production traffic patterns for 2-4 weeks to capture daily and weekly cycles. Use tools like Locust or k6 to replay these patterns at 1.5-2x normal volume. Include request payload variety since different input sizes affect inference time differently. Test both sustained load and spike scenarios. A common mistake is testing with uniform requests, which misses the latency variance from different input complexities.

For real-time predictions, aim for p50 under 50ms and p99 under 200ms. Batch inference is more flexible but should complete within SLA windows. The key metric is tail latency (p95/p99), not average, because users experience the worst cases. E-commerce recommendation models typically need sub-100ms; fraud detection needs sub-50ms. Set SLOs based on business impact, not technical convenience.

Run load tests after every model update, infrastructure change, or traffic pattern shift. At minimum, run monthly even without changes, as dependency updates and platform changes can silently affect performance. Automate load tests in your CI/CD pipeline for model deployments. Teams that skip regular load testing are often surprised by holiday traffic spikes or marketing campaign surges that overwhelm their serving infrastructure.