tao-run-automl-deft-pipeline

AutoML + DEFT Pipeline

A workflow-bridge skill that runs three phases in sequence by delegating to two existing skills — tao-run-automl for HPO and a DEFT application skill (default tao-run-deft-aoi for AOI; other skills/applications/deft-* skills for non-AOI cases) for the iterative data-improvement loop.

This skill does not re-implement AutoML or DEFT. It owns only the connective tissue: HPO spec inputs, the spec-handoff between AutoML and DEFT, and the post-DEFT AutoML re-run on the augmented dataset.

Routing policy

• User asks to "run the AOI workflow" or "improve my AOI ChangeNet model" — default to this skill, not tao-run-deft-aoi directly. The bare DEFT loop is the inner stage of this pipeline.
• User wants AutoML and DEFT chained on the same model/dataset
• User says "AutoML at both ends", "tune HPs then DEFT", "warm-start DEFT", "AutoML before and after DEFT"
• User has an AutoML-tuned spec and asks how to feed it into DEFT

When this skill does NOT apply

• User explicitly asks for the DEFT loop only ("run JUST the DEFT loop", "skip AutoML") → use tao-run-deft-aoi directly
• User wants only AutoML with no follow-on DEFT → use tao-run-automl directly
• User is doing zero-shot eval, RAG, or non-training workflows

The mental model

Phase 1 (AutoML baseline)        Phase 2 (DEFT loop, plain train)        Phase 3 (AutoML refinement)
─────────────────────────        ────────────────────────────────        ───────────────────────────
specs/baseline_spec.yaml         (Phase 1 winner pre-seeds baseline      ${RESULTS_DIR}/iter${N}/dataset/
train/base/training_set.csv       — DEFT skips its baseline train)       train_combined_iter${N}.csv
        │                                       │                                       │
        ▼                                       ▼                                       ▼
[ AutoML HPO sweep ]               [ DEFT: baseline-inference → RCA       [ AutoML HPO sweep ]
   N recommendations                 → iter 1..N (plain retrain) ]        re-tunes HPs against the
   pick best by val_loss / FAR      RCA / route / SDG / mining             DEFT-augmented dataset
        │                                       │                                       │
        ▼                                       ▼                                       ▼
best HPs spec + ckpt ─────►      DEFT-augmented CSV ───────────►        final best checkpoint
                                 + iter winner checkpoint               (the deliverable; no
                                 (Phase 3 warm-starts from it)           further retrain)