Hi Everyone,
I’m happy to announce the 0.11.0 release <https://github.com/intel-analytics/analytics-zoo/releases/tag/v0.11.0> of Analytics Zoo <https://github.com/intel-analytics/analytics-zoo/> (distributed TensorFlow and PyTorch on Apache Spark & Ray); the highlights of this release include: - Chronos <https://analytics-zoo.readthedocs.io/en/v0.11.0/doc/Chronos/Overview/chronos.html>: a new time-series analysis library with AutoML: - Built-in support of ~100 algorithms for time series forecast <https://analytics-zoo.readthedocs.io/en/v0.11.0/doc/Chronos/QuickStart/chronos-tsdataset-forecaster-quickstart.html> (e.g., TCN, seq2seq, ARIMA, Prophet, etc.), anomaly detection <https://analytics-zoo.readthedocs.io/en/v0.11.0/doc/Chronos/QuickStart/chronos-anomaly-detector.html> (e.g., DBScan, AutoEncoder etc.), and feature transformations (using TSDataset <https://analytics-zoo.readthedocs.io/en/v0.11.0/doc/Chronos/QuickStart/chronos-tsdataset-forecaster-quickstart.html> ). - Automatic tuning of built-in models (e.g., AutoProphet <https://analytics-zoo.readthedocs.io/en/v0.11.0/doc/PythonAPI/Chronos/autotsestimator.html#chronos-autots-model-auto-prophet> , AutoARIMA <https://analytics-zoo.readthedocs.io/en/v0.11.0/doc/PythonAPI/Chronos/autotsestimator.html#chronos-autots-model-auto-arima> , AutoXGBoost <https://analytics-zoo.readthedocs.io/en/v0.11.0/doc/Orca/QuickStart/orca-autoxgboost-quickstart.html>, etc.) using AutoML - Simple APIs for tuning user-defined models (including PyTorch and Keras) with AutoML <https://analytics-zoo.readthedocs.io/en/v0.11.0/doc/Orca/QuickStart/orca-autoestimator-pytorch-quickstart.html> - Improved APIs <https://analytics-zoo.readthedocs.io/en/v0.11.0/doc/PythonAPI/Chronos/index.html> , documentation <https://analytics-zoo.readthedocs.io/en/v0.11.0/doc/Chronos/Overview/chronos.html>, quick start examples <https://analytics-zoo.readthedocs.io/en/v0.11.0/doc/UserGuide/notebooks.html>, etc. - Reference implementation of large-scale feature transformation pipelines for recommendation systems (e.g., DLRM <https://github.com/intel-analytics/analytics-zoo/tree/branch-0.11/pyzoo/zoo/examples/friesian/feature/dlrm> , DIEN <https://github.com/intel-analytics/analytics-zoo/tree/branch-0.11/pyzoo/zoo/examples/friesian/feature/dien> , W&D <https://github.com/intel-analytics/analytics-zoo/tree/branch-0.11/pyzoo/zoo/examples/friesian/feature/wnd>, etc.) - Enhancements to Orca (scaling TF/PyTorch models to distributed Big Data) for end-to-end computer vision pipelines (distributed image preprocessing, training and inference); for more information, please see our CPVR 2021 tutorial <https://jason-dai.github.io/cvpr2021/>. - Initial Python and PySpark (in addition to Scala/Java) application support for PPML <https://analytics-zoo.readthedocs.io/en/v0.11.0/doc/PPML/Overview/ppml.html> (privacy preserving big data and machine learning) For more details, please see our github repo <https://github.com/intel-analytics/analytics-zoo> and document website <https://analytics-zoo.readthedocs.io/>. Thanks, -Jason
