- Update docker-compose.yml MODEL_PATH from best_model.keras to model.onnx
- Fix Machine_Learning/README.md TOC and section numbering (remove duplicate section 9, add Validasi Parity ONNX as section 9)
- Clarify parity validation as manual/recommended, not mandatory CI
- Update CLAUDE.md to document Rust/Axum/ONNX Runtime ML service and ONNX export workflow
- Simplify root README.md ML service section with port clarification (8001 local, 8000 container)
- Remove stale endpoint examples from root README (documented in apps/ml-service/README.md)
- Ensure no FastAPI/Uvicorn references in deployment documentation
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Update documentation to reflect migration from FastAPI/Uvicorn to Rust/Axum with ONNX Runtime:
- Root README: Update ML service description, tech stack, prerequisites, and run instructions to use Rust/Cargo instead of Python/Uvicorn
- Root README: Update model path references from best_model.keras to model.onnx
- Root README: Add model.onnx to artifact lists and generated files
- Root README: Update troubleshooting section with Rust-specific guidance
- Machine_Learning/README: Add table of contents entry for ONNX conversion
- Machine_Learning/README: Add Tahap 5 section documenting ONNX conversion with convert_onnx.py and validate_onnx_parity.py
- Machine_Learning/README: Update output table to include model.onnx with Rust ONNX Runtime usage
- apps/ml-service/README: Create comprehensive documentation for Rust Axum ONNX service including setup, endpoints, environment variables, testing, and troubleshooting
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
