Batching specialists for your dedicated team.

Where batching fits in a long-term product team.

The throughput gain is disproportionate to the latency cost. Waiting ten milliseconds to collect a batch might multiply throughput several times over, which on GPU serving is the difference between one instance and several.

The trade has to be set per workload. An interactive interface has a tight latency budget; a background scoring job has effectively none, and should batch as aggressively as memory allows. Using one configuration for both leaves throughput or responsiveness on the table.

What your dedicated team does with batching.

Dynamic batching parameters need tuning against real traffic. The optimal window depends on arrival rate and its variability, and a setting chosen from a load test with uniform traffic will not match production.

Tuning against real traffic patterns is ordinary operational rigour, held within an agreed committed monthly capacity.

Common batching use cases.

• Throughput multiplied for small latency A short collection window that substantially reduces the hardware needed.
• Different settings per workload Interactive and batch paths configured separately, each for its own needs.
• Tuned against real traffic Window sizes set from production arrival patterns, not a uniform load test.

Adding batching skills to your team.

Serving optimisation sits with the ML specialists we assign to you, with batching configured per workload.

At Azendo, batching work sits with a dedicated MLOps team. Every batching specialist we assign works remotely inside your tools as a dedicated member of your team, with the management on our side. It is how companies hire remote developers without opening an offshore office of their own.