| ECO-AIM-AI-001 |
Oversized model selection |
warning |
AI |
| ECO-AIM-AI-002 |
No inference batching |
warning |
AI |
| ECO-AIM-AI-003 |
Re-embedding unchanged data |
warning |
AI |
| ECO-AIM-AI-004 |
No prompt caching |
note |
AI |
| ECO-AIM-AI-005 |
Always-on inference endpoints |
warning |
AI |
| ECO-AIM-AI-006 |
Unbounded context window usage |
warning |
AI |
| ECO-AIM-AI-007 |
No model quantization |
note |
AI |
| ECO-AIM-AI-008 |
Re-training without drift detection |
warning |
AI |
| ECO-AIM-AI-009 |
No evaluation before scaling model |
warning |
AI |
| ECO-AIM-AI-010 |
Overly frequent fine-tuning cycles |
note |
AI |
| ECO-AIM-AI-011 |
Storing all embeddings indefinitely |
warning |
AI |
| ECO-AIM-AI-012 |
Large model in low-SLA workload |
warning |
AI |
| ECO-AIM-AI-013 |
No GPU utilization monitoring |
warning |
AI |
| ECO-AIM-AI-014 |
Inefficient feature preprocessing pipelines |
note |
AI |
| ECO-AIM-AI-015 |
No batching of vector search queries |
note |
AI |