Defined canonical ML run artifacts and diagnostics

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Defined canonical ML run artifacts and diagnostics

  • Day: 2026-05-17
  • Time: 11:10 to 11:20
  • Project: Dev
  • Workspace: WP 1: Strategic / Growth & Development
  • Status: In Progress
  • Priority: HIGH
  • Assignee: Matías Nehuen Iglesias
  • Tags: Ml-Runtime, Experiment-Tracking, Baseline-Training, Residual-Analysis, Artifact-Management, Thesis

Description

Session Goal

Advance the thesis ML pipeline from debug/runtime validation into a reproducible baseline-training and diagnostics workflow, with clear ownership of experiment artifacts.

Key Activities

  • Reviewed the first successful end-to-end ML runtime and the transition from debug mode toward baseline training.
  • Specified inspection checks for debug artifacts before merging, including runtime safety and repository-state validation.
  • Drafted a Codex brief for baseline training that includes required metrics, guardrails, tests, and a safety flag for controlled execution.
  • Designed a thesis-oriented diagnostic layer beyond scalar metrics: residual plots, error stratification by income decile, grid-search sensitivity analysis, and artifact archiving.
  • Framed metric storage as an evidence-architecture problem, emphasizing canonical ownership of predictions, residuals, metrics, and derived artifacts.
  • Proposed a canonical local run-artifact hierarchy inspired by MLflow concepts, without adding MLflow as a dependency yet.
  • Established the run directory as the canonical source of truth for future diagnostics, with plots treated as disposable views derived from archived predictions and CV results.

Achievements

  • Clarified the repository progression from debug execution to baseline training.
  • Defined a reproducible artifact model for experiments, centered on run manifests, stacked prediction tables, CV result exports, and summary diagnostics.
  • Established the next implementation direction: a diagnostics layer that reads archived run artifacts without retraining or re-predicting.
  • Improved decisional intent around provenance, lineage, and evidence preservation for thesis reporting.

Pending Tasks

  • Implement the baseline training workflow with the specified guardrails and tests.
  • Persist predictions, residuals, and cv_results_ in the canonical run directory.
  • Build the diagnostics layer that consumes archived artifacts and generates plots/reports without retraining.
  • Validate the artifact ownership convention and ensure merge checks enforce the new run structure.

Evidence

  • source_file=2026-05-17.sessions.jsonl, line_number=5, event_count=0, session_id=aba3d341baf55631e9f472506684b9d124a64ef49a96ac2f26f97ffa6656657a
  • event_ids: []

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