9 commits

Author	SHA1	Message	Date
Marko Djordjevic	28ebe2c5d1	feat(ui): add prediction state management and PredictionPanel component ... - Create prediction type definitions in src/types/predictions.ts - Add prediction state management to page.tsx with caching - Implement PredictionPanel component with: - Master visibility toggle - Model info display (name, version, type, metrics) - Action buttons (Run on Visible, Predict All) - Confidence threshold slider - Label filter checkboxes with per-class metrics - Disagreement filter toggle - Prediction summary display - Model server offline banner - Add on-demand and batch prediction fetching - Implement prediction caching by chart and model version - Add health polling for inference API (30s interval when offline) - Ensure annotation tools work independently of prediction API Tasks completed: 9.1-9.5, 12.1-12.3 (59/78 total)	2026-02-15 16:20:07 +01:00
Marko Djordjevic	bb1b6d573f	feat(api): add span annotation export and feedback loop support ... - Add GET /api/span-annotations/export endpoint for ML pipeline JSON/CSV export - Add source and model_prediction fields to span_annotations schema - Update POST endpoint to accept source (human/model/human_correction) and model_prediction metadata - Support negative annotations (label 'O' for user corrections to model predictions) - Create migration 0005 for new schema fields Completes tasks 8.1-8.4 of candle-backend change	2026-02-15 14:35:31 +01:00
Marko Djordjevic	205021e810	feat(api): add Next.js proxy routes for ML inference service	2026-02-15 14:30:09 +01:00
Marko Djordjevic	3a83fd38e9	feat(ml): implement FastAPI inference service with model loading, preprocessing, and prediction endpoints	2026-02-15 14:29:07 +01:00
Marko Djordjevic	f4c0f9a836	feat(ml): implement training stage with MLflow tracking and model wrappers ... - Create RandomForestModel and XGBoostModel wrappers with class weight support - Implement temporal and random train/val/test splitting - Add MLflow experiment tracking with full parameter and metric logging - Create evaluation module for confusion matrix, feature importance, and classification reports - Implement model training with sklearn/xgboost flavor logging and optional registry registration - Store training run metadata in PostgreSQL - Wire training stage into pipeline.py orchestrator - Support both RandomForest and XGBoost models with configurable hyperparameters	2026-02-15 14:22:19 +01:00
Marko Djordjevic	16763b967e	feat(ml): implement annotation ingestion with windowed/BIO encoding and TA-Lib patterns	2026-02-15 12:28:58 +01:00
Marko Djordjevic	fd29ab91e0	feat(ml): implement feature engineering pipeline ... - Create pipeline.py with CLI argument parsing for running stages - Implement TA-Lib indicator computation with multi-output support - Add candle feature extraction (body_size, wicks, ratios, etc.) - Create custom feature loader with dynamic module import - Wire all feature engineering stages with NaN handling - Tasks completed: 2.2, 2.3, 3.1, 3.2, 3.3, 3.4, 3.5	2026-02-15 12:22:59 +01:00
Marko Djordjevic	ea339a54a7	feat(ml): add database schema, config parser, and DVC setup ... - Initialize DVC with local storage backend (task 1.6) - Create PostgreSQL schema for training_runs table (task 1.7) - Add SQLAlchemy database connection setup (task 1.8) - Create Pydantic config models for pipeline.yaml (task 2.1) - Add migration runner for database setup - Fix pyproject.toml package discovery config	2026-02-15 12:08:53 +01:00
Marko Djordjevic	1a653c5866	feat: add ML service scaffolding with Python FastAPI, Docker, and MLflow setup	2026-02-15 11:58:31 +01:00