In 2023, our top priorities are:
- Meta-Learning
- Pre-Training
- Exploratory Data Analysis
- Enhanced Cloud Integration
- Website Overhaul
- Documentation & Tutorial Overhaul
- Model Fairness Analysis
- Enhanced Model Distillation
- Model Compilation & Optimization
- Distributed Model Training
- Distributed Hyperparameter Tuning
- Improved Large-scale Data Handling (100M+ Rows)
- Improved Feature Type Inference
- Enhanced Tabular GPU Support
- Co-variate Shift Detection
- Co-variate Shift Correction
- Model Interpretability
- Model Uncertainty
- Model Monitoring
- Advanced Custom Model Tutorial
In 2022, our top priorities are:
- (v0.4) Windows OS Support
- (v0.4) Python 3.9 Support
- (v0.5) Time-Series Support (autogluon.timeseries)
- (v0.4) HuggingFace Integration
- (v0.5) TIMM Integration
- (v0.5) Improved Multi-modal Modeling (autogluon.multimodal)
- (v0.4) Cloud Training & Deployment
- (v0.4) Parallel Model Training
- (v0.6) Parallel Hyperparameter Tuning
- (v0.4) Semi-supervised Learning
- (v0.4) Automated Model Calibration via Temperature Scaling
- (v0.4) Online Inference Optimization
- (v0.5) Improved Large-scale Data Handling (10M+ Rows)
- (v0.6) Faster Feature Preprocessing
- (v0.6) Image Model Inference Optimization
- (v0.6) Text Model Inference Optimization
- Named Entity Recognition Support
- Object Detection Support in autogluon.multimodal
- Multimodal Matching Support
- Parallel Hyperparameter Tuning
- Image Model Inference Optimization (~10x faster for online inference)
- Text Model Inference Optimization (~10x faster for online inference)
- FT-Transformer model to Tabular
- Faster Feature Preprocessing (~25%)
- Few-shot learning with 11B-scale models on single GPU
- Prototype model compilation support to Tabular
- Time-Series Support (autogluon.timeseries)
- Improved Multi-modal Modeling (autogluon.multimodal)
- TIMM Integration
- imodels Integration
- Windows OS Support
- Python 3.9 Support
- HuggingFace Integration
- Torch Migration (Remove MXNet dependency)
- Parallel Model Training (2x training speed-up for bagging/stacking)
- Automated Feature Pruning/Selection
- Semi-supervised & Transductive Learning Support
- Automated Model Calibration via Temperature Scaling
- Cloud Training & Deployment Tutorials
- Feature Preprocessing Tutorial
- Documentation Overhaul
- Hyperparameter Tuning Overhaul
- Memory Usage Optimizations
- Various Performance Optimizations
- Various Bug Fixes
In 2021, our top priorities are:
- Make AutoGluon the most versatile AutoML framework via dedicated multi-modal image-text-tabular support (paper).
- Modularization of the various components of AutoGluon.
- Model Training Speed Optimizations.
- Model Inference Speed Optimizations.
- Model Quality Optimizations.
- Integration with NVIDIA RAPIDS for accelerated GPU training.
- Integration with Intel sklearnex for accelerated CPU training.
- Improved documentation and tutorials.
- Training and Inference containers.
In 2020, we plan to focus on improving code quality, extensibility, and robustness of the package.
We will work towards unifying the APIs of the separate tasks (Tabular, Image, Text) to simplify and streamline development and improve the user experience.
- v0.0.15 Release Notes (December 2020)
- v0.0.14 Release Notes (October 2020, Highlight: Added FastAI Neural Network Model)
- v0.0.13 Release Notes (August 2020, Highlight: Added model distillation (paper))
- v0.0.12 Release Notes (July 2020, Highlight: Added custom model support)
- v0.0.11 Release Notes (June 2020)
- v0.0.10 Release Notes (June 2020, Highlight: Implemented feature importance)
- v0.0.9 Release Notes (May 2020)
- v0.0.8 Release Notes (May 2020)
- v0.0.7 Release Notes (May 2020, Highlight: first addition of the
presetsargument) - v0.0.6 Release Notes (March 2020, first release tagged on GitHub with release notes)
- v0.0.5 Release (February 2020, used in the original AutoGluon-Tabular paper)
- v0.0.4 Release (January 2020)
In 2019, we plan to release the initial open source version of AutoGluon, featuring Tabular, Text, and Image classification and regression tasks, along with Object Detection.
- v0.0.3 Release (December 2019)
- v0.0.2 Release (December 2019, Initial Open Source Release)