Description: Build automated ML model training, evaluation, and deployment pipeline with historical backtesting, model comparison, and A/B testing capabilities.
Difficulty: Hard
Acceptance Criteria:
- Automated data pipeline for training set preparation
- Model training with cross-validation
- Hyperparameter tuning (grid search, random search)
- Model performance comparison framework
- Statistical significance testing
- A/B testing infrastructure for model deployment
- Automatic retraining triggers (data drift detection)
- Model versioning and rollback
- Training metrics dashboard
Estimated Effort: 1,500 lines of code
- 4 services (data preparation, model trainer, evaluator, deployer)
- 3 entities (training jobs, model versions, performance metrics)
- Model persistence and versioning
- 25+ tests
- Historical backtest integration
Description: Build automated ML model training, evaluation, and deployment pipeline with historical backtesting, model comparison, and A/B testing capabilities.
Difficulty: Hard
Acceptance Criteria:
Estimated Effort: 1,500 lines of code