AI in production: what you need beyond the model

Define the product requirements

Before choosing a GPU, define the user flow. An interactive conversation, a document batch and live transcription require different limits for waiting, concurrency and recovery.

An infrastructure review identifies models, input-size distribution, volume by time of day and latency targets. These inform the test workload and acceptance criteria.

Organise inference integration

• An API with client-specific access, input limits and concurrency control.
• A bounded queue for asynchronous work, with safe retries and cancellation.
• Keep data and secrets out of logs; give each service only the access it needs.
• Identify the model, image and configuration versions for every deployment.

Monitor operations and results

A service can pass availability checks while its queue remains blocked. Track waiting time, latency, errors, GPU memory and delivered results. Distinguish provider, inference and integration failures.

Alerts need assigned owners and agreed actions. Monitoring should include response procedures as well as dashboard metrics.

Validate recovery procedures

Document how to roll back a version, resume a queue and restore required data. Test restoration: having a backup file does not prove the product can recover.

Consulting follows an agreed deployment and operations scope. Capacity, availability and support coverage are defined in the proposal from the product's needs.