Backup and recovery for an AI platform

Define what the product needs to recover

A platform can keep metadata in a database, documents in separate storage and models on processing servers. The queue depends on recovery positions and confirmed results. Restoring one part does not ensure the others match the same version.

Start with the flow the company needs to recover. Identify essential data, data that can be rebuilt and required access. Define acceptable data loss and the desired recovery time before choosing backup frequency.

Create the copy without ignoring database operations

In MySQL operations I worked on, backup preparation considered a replica, available space and a disk separate from active data. The flow included preparing the copy and the position needed to relate it to replication. The aim was to control backup impact on the main workload.

A replica can also receive an incorrect deletion. A copy on the same server shares disk and access risks. The design must assess independent destinations, retention and protection of the keys required for restoration.

Rehearse restoration of the complete flow

Restore into a compatible environment and check required data. Then validate documents, model versions, permissions and queue positions. The exercise must prevent a restored copy from sending messages or executing production external effects.

Measure the actual procedure time and record its dependencies. A desired recovery time is not a proven recovery time. An intact backup directory also does not prove that the service resumed useful results within that period.

Prepare continuity with clear responsibilities

Consulting can inventory dependencies, review backups and prepare a recovery exercise. The contract defines retention, destinations, access, owners and acceptance criteria. Coverage follows the product's risk and budget.