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case-study

ClearML Experiment Tracking for Dermaself

MLOps case study for setting up ClearML tracking around Dermaself skin-analysis experiments, run metrics, and promotion gates.

Public-safe MLOps card showing Dermaself ClearML experiment tracking with datasets, parameters, metrics, artifacts, QA gates, and promotion boundaries

Overview

ClearML Experiment Tracking for Dermaself captures the MLOps layer behind the Dermaself skin-analysis work. The public entry focuses on setting up ClearML-backed experiment tracking for model runs, dataset and parameter hygiene, metric review, artifact boundaries, and promotion decisions around the same public-safe Dermaself CV pipeline. It deliberately avoids publishing raw skin images, private datasets, model weights, ClearML server URLs, or user-level records.

What It Covers

  • Sets up ClearML experiment tracking for Dermaself model runs without exposing private workspaces
  • Keeps datasets, parameters, metrics, artifacts, and promotion decisions reviewable across CV iterations
  • Separates debug or overfit experiment notes from release-ready mobile and server claims
  • Keeps raw skin images, private datasets, model weights, and ClearML server URLs out of public portfolio files

Stack And Topics

  • ClearML
  • Python
  • PyTorch
  • ONNX
  • TFLite
  • Flutter
  • Computer Vision
  • MLOps
  • Experiment Tracking

Public Signals

  • Tracking stack: ClearML Dermaself MLOps setup added to public portfolio scope, 2026-06-09
  • Tracked surfaces: 5 dataset, parameters, metrics, artifacts, and promotion decisions
  • Public posture: sanitized public case study excludes raw skin photos, private datasets, model weights, and ClearML server URLs
  • Promotion boundary: review-gated debug experiments stay separate from release-ready mobile/server claims

References

This case-study page is generated from the public portfolio dataset and exists as the search-facing canonical URL for ClearML Experiment Tracking for Dermaself.