Highlights
- [Chronos](https://analytics-zoo.readthedocs.io/en/v0.11.0/doc/Chronos/Overview/chronos.html): an overhaul of the previous Zouwu time-series analysis library, with:
- 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 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)