slug: determined-ai
provider: Determined AI
generated_by: planning/capability-mapping/scripts/classify_capabilities.py
model: claude-opus-5
frame:
- Software & Technology
min_confidence: 0.7
capability_model:
  source: https://github.com/vincentmakes/turbo-ea-capabilities
  license: CC-BY-4.0
  attribution: Turbo EA Capabilities by Vincent Verdet — Turbo EA, https://github.com/vincentmakes/turbo-ea-capabilities,
    CC BY 4.0
  notice: NOTICE
edge_count: 3
edges:
- tag: Models
  spec_file: determined-ai-models-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.85
  evidence: 'GET /api/v1/models  getModels  List models; schemas: Model'
  reason: Model registry listing on a deep-learning training platform directly realises AI/ML model lifecycle
    management.
- tag: Experiments
  spec_file: determined-ai-experiments-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.8
  evidence: GET /api/v1/experiments  getExperiments  List experiments; 'without needing to worry about
    DevOps or writing custom code for common tasks like fault tolerance or experiment tracking'
  reason: Experiments here are deep-learning training runs tracked by the platform — ML model lifecycle
    / MLOps, not product A/B experimentation.
- tag: Checkpoints
  spec_file: determined-ai-checkpoints-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.7
  evidence: 'GET /api/v1/checkpoints/{checkpointUuid}  getCheckpoint  Get checkpoint; schemas: Checkpoint'
  reason: Model training checkpoints are artefacts of the ML model lifecycle on a deep-learning training
    platform; fits Artificial Intelligence Management, though a single read operation makes the evidence
    modest.