Baseline the lifecycle
Map datasets, code, environments, training methods, artifacts, evaluation authority, and release flow.
Make every model artifact traceable to the data, objective, code, environment, evidence, and authority that produced it.
We review or design pipelines for pretraining, continued pretraining, supervised fine-tuning, preference optimization, reinforcement learning, distillation, and adapter-based specialization. Security, reproducibility, provenance, data quality, and evaluation are treated as one lifecycle.
The engagement can focus on a specific training run or the full operating model from dataset admission through registry, promotion, deployment, drift monitoring, rollback, and incident evidence. Recommendations remain platform-aware and implementation-ready.
Often requested as
Model training consultingMLOps security reviewAI pipeline evaluationModel governanceLLM evaluation and promotion