The model you shipped last quarter doesn't know what happened last month.
Enterprise knowledge moves fast. Product documentation changes, internal policies are revised, new terminology emerges from customer conversations. Your model doesn't know any of it until the next retraining cycle begins.
A full retraining sprint means weeks of GPU time, ML team attention, eval coordination, and deployment windows. The overhead scales poorly as your knowledge base grows — and it has to repeat every time knowledge drifts.
In the gap between training cycles, your production model answers questions using stale context. For customer-facing deployments, that means wrong answers. For internal workflows, it means degraded accuracy on the queries that matter most.
Five steps. Continuous loop. Zero downtime.
The mechanism runs inside the 4MINDS platform. No separate fine-tuning infrastructure, training clusters, or deployment pipelines.
The eval gate runs configurable task benchmarks — domain-specific prompts from your actual use case. Pass threshold is set by your team. Failure means the shadow is discarded; production is unchanged. Every run is timestamped.
Production swap creates a checkpoint. If a deployed model causes unexpected behavior, one command reverts to the prior version. History retained for audit.
Ghost weights training runs as a Kubernetes job inside your existing cluster. No separate GPU cluster or external training API. Air-gapped compatible.
What a retraining sprint actually costs.
- 01Detect model staleness via user complaints or accuracy drop
- 02Scope and schedule a retraining sprint (1–2 weeks planning)
- 03Collect, clean, and version training data (3–5 days)
- 04GPU training run (1–7 days depending on model size)
- 05Internal eval, regression testing, sign-off (1 week)
- 06Coordinate deployment window and rollout (1–3 days)
- 01New data flows in automatically — no accumulation window needed
- 02Shadow copy trains continuously — no sprint scheduling required
- 03Data versioning handled by the platform — no manual prep
- 04Training runs continuously in background — no dedicated window
- 05Eval gate runs automatically on every candidate — no manual sign-off
- 06Atomic swap with zero downtime — no deployment coordination
Ghost Weights vs. the alternatives.
| Criterion | Ghost Weights | Manual Fine-Tuning | Static Model |
|---|---|---|---|
| Update latency | Hours | 3–8 weeks | Never |
| Downtime required | None | Yes (deploy window) | N/A |
| MLOps overhead | Minimal (automated) | High (sprint cycle) | None |
| Eval gate before swap | Yes (configurable) | Optional | N/A |
| Audit trail | Timestamped per swap | Manual record | None |
| Rollback capability | Instant | Manual redeploy | N/A |
| Air-gap compatible | Yes | Depends on infra | Yes |
Every model update is audited before it goes live.
The eval gate is not optional. It runs before every swap.
- ›Benchmark task suite (domain-specific, configurable)
- ›Regression check: shadow must outperform live on benchmark
- ›Compliance flag detection (configurable keyword/pattern list)
- ›Manual approval gate (optional — for regulated environments)
{
"swap_id": "gw_20260404_0847",
"model_from": "4minds-v2.4.1",
"model_to": "4minds-v2.4.2",
"eval_run_at": "2026-04-29T08:04:17Z",
"eval_result": "PASS",
"benchmarks": {
"domain_accuracy": {
"live": 0.87, "shadow": 0.91
},
"regression_score": {
"live": 0.94, "shadow": 0.95
},
"compliance_flags": 0
},
"swapped_at": "2026-04-29T08:06:00Z",
"operator": "automated",
"rollback_to": "4minds-v2.4.1"
}Eval gate output is built for audit trail requirements. Every swap, every version, every score — timestamped and retained. Your compliance team controls the log.
See Ghost Weights on your data.
30 minutes with a 4MINDS engineer. We'll deploy a live ghost weights instance against a sample of your enterprise knowledge base and show you update latency end-to-end.