slug: inferless
provider: Inferless
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: 2
edges:
- tag: Model Management
  spec_file: inferless-model-management-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.72
  evidence: '"Update a model''s autoscaling and machine settings"; "Retrieve runtime logs for a deployed
    model"; schema ModelSettingsUpdateRequest'
  reason: Operational management of deployed ML model deployments (settings, autoscaling, logs) is MLOps,
    within AI/ML model lifecycle management. Some overlap with platform operations keeps confidence below
    0.8.
- tag: Inference
  spec_file: inferless-inference-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.7
  evidence: POST /v2/inference/{model_name}/infer — "Run inference against a deployed model"; schemas
    InferenceRequest, InferenceTensor
  reason: Serves predictions from deployed ML models (KServe-style), which is AI/ML model lifecycle and
    serving; no industry-specific capability applies to a serverless GPU inference devtool.