Keep models current with continual pre-training, model merging, and knowledge distillation. Update models incrementally without catastrophic forgetting while adapting to new domains and data.
Medical LLM adaptation, clinical knowledge updates, diagnostic model evolution
Market model updates, regulatory compliance adaptation, fraud pattern evolution
Search model updates, recommendation freshness, content model adaptation
Trend adaptation, seasonal model updates, inventory prediction evolution
Process optimization updates, quality model evolution, equipment adaptation
Evaluate current model capabilities and identify knowledge gaps or domain adaptation needs
Curate high-quality data for continual learning, balancing new and replay data
Apply re-warming strategies and regularization to prevent catastrophic forgetting
Combine specialized fine-tuned models using SLERP, TIES, or DARE techniques
Transfer capabilities from large teachers to efficient student models
Verify no regression on existing capabilities before production deployment
| Component | Function | Tools |
|---|---|---|
| Continual Pre-training | Domain adaptation, knowledge updates with forgetting prevention | LLaMA PRO, ConPET, EWC |
| Model Merging | Combine fine-tuned models without additional training | MergeKit, PEFT, mergeoo |
| Knowledge Distillation | Transfer knowledge from large to small models | DistilBERT, TinyBERT, PGKD |
| Replay Systems | Maintain performance on previous tasks | Experience replay, pseudo-rehearsal |
| Evaluation | Catastrophic forgetting detection, capability assessment | lm-evaluation-harness, custom benchmarks |
| Orchestration | Automated continual learning pipelines | Kubeflow, Airflow, MLflow |
Let us help you build systems that keep your AI current without costly retraining.
Get Started