Enhance AutoML functionality with class label handling, improved feature engineering, and refined prediction outputs. Update FastAPI integration for better state management and progress reporting during training and tuning processes. Improve Streamlit UI for displaying class probabilities in predictions.
Implement AutoML pipeline with ingestion, preprocessing, model training, hyperparameter tuning, and explainability. Enhance FastAPI integration for async training and prediction, and improve Streamlit UI for user configuration.
Implement FastAPI backend for async training, prediction, and explainability. Add job state management, training configuration, and endpoints for training, status, leaderboard, prediction, and model export. Enhance Streamlit UI for dataset upload, training progress tracking, and model explainability visualization.
Implement model explainability using SHAP, including global importance, summary plots, and single-row waterfalls. Add abstract base model class for model wrappers, create a model zoo for various estimators based on dataset size and task type, and develop a cross-validation trainer for model evaluation. Integrate MLflow for experiment tracking, logging dataset stats, model results, and SHAP plots. Introduce Optuna-based hyperparameter tuning for top models from the leaderboard, with parameter suggestion spaces for various algorithms.
