NamePo engineers scope, deliver, and document custom ai training & fine-tuning with clear milestones, staging validation, and handover notes.
À partir de
USD 540
Custom fine-tuning adapts a base model to your domain using curated labeled examples, improving consistency on classification, extraction, and tone tasks where generic prompts drift. We audit whether fine-tuning is appropriate versus RAG or structured prompting, clean and split your dataset to prevent leakage, run evaluation against holdout sets, and guard against overfitting and unsafe outputs before deployment. Production serving uses the same API integration patterns as base models with versioned rollback.
1. Approach selection
We compare fine-tuning, RAG, and advanced prompting on a sample set. Proceed with fine-tune only if measurable lift justifies maintenance cost.
2. Dataset audit & preparation
Duplicates removed, label inconsistencies resolved, train/validation/test splits stratified to prevent leakage from near-duplicate rows.
3. Training & evaluation cycles
Hyperparameters swept within budget. Checkpoints scored on holdout metrics and manual review of worst errors.
4. Safety review & deployment
Adversarial prompts tested. Winning checkpoint deployed behind existing API layer with monitoring for drift.
| Facteur de décision | Cette approche | Alternative courante | Notes |
|---|---|---|---|
| Approach fit analysis | Documented comparison of fine-tune vs RAG vs prompts on your sample set | Fine-tune recommended because it sounds advanced | Unnecessary fine-tunes incur retraining cost when RAG would suffice. |
| Dataset hygiene | Leakage checks, deduplication, and label consistency audit | Raw CSV uploaded directly to training job | Duplicate rows inflate metrics and fail on fresh production inputs. |
| Evaluation rigor | Holdout metrics plus worst-case manual error review | Training loss curve only | Loss curves hide catastrophic failures on minority classes. |
| Production safety | Adversarial eval and checkpoint rollback wired before traffic | Deploy latest epoch automatically | Later epochs often overfit and increase unsafe completions. |
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