[Announcement] Analytics Zoo 0.11.0 release

[Jason Dai <ja...@gmail.com>]

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