Fine-Tuning Pipeline
Curate a training set, fine-tune a base model, and register the result.
AdvancedAI / ML
Labelled examples are curated and validated, used to fine-tune a base model, evaluated against a held-out set, and -- if it clears the bar -- registered as a new versioned model ready for serving.
When to use it
- A base model's generic behaviour needs specialising to a narrow domain or house style
- You have (or can generate) enough quality-labelled examples to fine-tune meaningfully
Trade-offs
- Requires an ongoing data curation and evaluation discipline, not a one-off run
- A fine-tuned model needs re-training as the base model or requirements evolve
Components used
Object StorageModel TrainingBatch / Scheduled JobModel Registry
How it works
- Raw examples are curated, filtered and split into train and eval sets. This curation step dominates final quality far more than any hyperparameter.
- A training job fine-tunes a base checkpoint, then an evaluation gate compares the result against the incumbent on a held-out set.
- Passing models are written to a registry with their dataset version and metrics, so any deployed model can be traced back to exactly what it learned from.
Used in the wild
- Teaching a model a house style, tone or strict output format that prompting keeps failing to enforce.
- Compressing a large expensive model into a small fast one for a narrow task.
- Domain adaptation for specialist vocabulary — legal, clinical or industrial jargon.
Good to know
- Fine-tuning teaches form far more reliably than it teaches facts. If the goal is up-to-date knowledge, RAG is usually the correct tool and fine-tuning is an expensive detour.
- LoRA and similar adapter methods train a small number of extra weights instead of the whole network, which is why fine-tuning stopped requiring a cluster around 2021.
Related patterns
Retrieval-Augmented Generation (RAG)
Ground an LLM's answers in retrieved, up-to-date, private documents.
Agentic Tool-Use Loop
An agent plans, calls tools, observes results, and iterates to a goal.
Vector Search + Rerank
Cheaply retrieve a broad candidate set, then precisely re-rank the top results.
Model Serving with A/B Testing
Route inference traffic across model versions to compare live performance.