Project: replace a ticket classifier after a training-record removal

Last updated: 6 Oct 20265 min read

project

Trace affected ticket IDs, rebuild from a filtered snapshot and stop a stale model from returning through a registry restore.

Map the approved request

An internal data owner approves removal of ticket 82 from a support-ticket classifier. Map the ID to raw and normalized records, snapshots, derived caches, model runs and a batch-scoring job. Discover two affected model digests, including one archived revision that is absent from the current production pointer. The impact map prevents an apparently unused archived artifact from escaping review.

Rebuild cleanly

Apply a stable tombstone before the next source import and create a new snapshot without the ticket or its derived copies. Do not resume from an affected checkpoint. Train a candidate, recalibrate if required, and pass response-contract and rare-ticket quality gates. Verify the manifest exclusion and store the approved scope, while avoiding a claim that this one check proves every storage copy is gone.

Catch the restore failure

Move the production and batch consumers to the replacement. Then rehearse a registry restore from an older backup. The restored metadata tries to point a shadow endpoint at the affected model; reconciliation rejects that pointer and keeps the digest unavailable. Release reconciliation holds completion until every affected consumer is replaced or disabled. Record the backup disposition separately from the new model’s quality result.

Handoff the evidence

Deliver request ID, source tombstone revision, filtered snapshot digest, parent checkpoint proof, candidate evaluation, affected artifact inventory, consumer states and restore-rehearsal result. Note any unresolved retention exception for the authorized data owner rather than guessing. Restore controls and retirement gates keep the old route from reappearing.

Implementation

def ticket_removal_completion(affected_artifacts, consumers, blocked_digests):
    for consumer in consumers:
        if consumer["model_digest"] in affected_artifacts and consumer["active"]:
            return "hold:old-model-active"
    if not affected_artifacts.issubset(blocked_digests):
        return "hold:restore-allowlist"
    return "close:artifact-scope"

affected = {"router-r8", "router-r9"}
consumers = [{"model_digest": "router-r10", "active": True},
             {"model_digest": "router-r8", "active": False}]
assert ticket_removal_completion(affected, consumers, affected)        == "close:artifact-scope"
assert ticket_removal_completion(affected, consumers, {"router-r8"})        == "hold:restore-allowlist"

Performance and operating cost

The completion check is O(c + a) expected time and O(1) extra space for c consumers and a affected digests. The full workflow costs a clean training run, quality evaluation and consumer migration. Inventory and restore tests add operational work, but a replacement that leaves an active old model has not achieved its stated artifact scope.

Common Mistakes

Ignoring archived artifacts because only the current production model is visible.
Resuming training from a checkpoint that already used the removed record.
Marking completion while a batch consumer still uses the old digest.
Assuming a registry restore cannot reactivate a disabled revision.