Design a self-service fine-tuning platform for internal teams (upload data → train → evaluate → deploy).

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Teams upload datasets through a validated pipeline (schema checks, PII scans, dedup, train/test split). Jobs are configured from templates (base model, LoRA or full fine-tune, hyperparameters) and run as job orchestration on a GPU cluster with quotas, checkpointing and experiment tracking. Automatic evaluation compares against the base model and current production on task and safety suites, then a model registry handles approval and deployment to serving (multi-LoRA serving for efficiency), with monitoring and rollback.

A dataset is uploaded, validated and versioned, then a job config goes to a GPU scheduler and training workers; the result is auto-evaluated, stored in a model registry, passes an approval gate, and is served with multi-LoRA under monitoring and rollback.

Key components

Data governance: consent and usage rights, PII redaction, licence checks; dataset versioning and lineage.
Templates and guardrails: sensible defaults (LoRA rank, LR, epochs); prevent common mistakes (missing EOS tokens, chat template mismatch, train/test leakage).
Compute management: GPU quotas per team, priority queues, preemption with checkpoint/resume, cost reporting.
Experiment tracking: metrics, configs and artifacts (MLflow / W&B-style).
Evaluation gate: must beat the baseline on the task eval and not regress safety or general abilities beyond thresholds.
Serving efficiency: many LoRA adapters on one base model (multi-LoRA serving) instead of a GPU per fine-tuned model.
Lifecycle: retraining when data drifts; deprecating adapters when the base model is upgraded (re-train on the new base).

Success metrics: time from data to deployed model, GPU utilisation, percentage of fine-tunes beating prompting baselines, incidents.

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Design a self-service fine-tuning platform for internal teams (upload data → train → evaluate → deploy).

Key components

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