# Overview

![logo](Image/logo_s.jpg) 

_A unified Data Analytics and AI platform for **distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray**_

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##  **What is Analytics Zoo?**

Analytics Zoo seamless scales TensorFlow, Keras and PyTorch to distributed big data (using Spark, Flink & Ray).



![blockdiagram](Image/blockdiagram.jpg) 


* **End-to-end pipeline for applying AI models (TensorFlow, PyTorch, OpenVINO, etc.) to distributed big data** 
    * Write [TensorFlow](https://analytics-zoo.github.io/master/#ProgrammingGuide/TFPark/tensorflow/) or [PyTorch](https://analytics-zoo.github.io/master/#ProgrammingGuide/pytorch/) inline with Spark code for distributed training and inference.
    * Native deep learning (TensorFlow/Keras/PyTorch/BigDL) support in [Spark ML](https://analytics-zoo.github.io/master/#ProgrammingGuide/nnframes/) Pipelines.
    * Directly run [Ray](https://analytics-zoo.github.io/master/#ProgrammingGuide/rayonspark/) programs on big data cluster through _RayOnSpark_. 
    * Plain Java/Python APIs for (TensorFlow/PyTorch/BigDL/OpenVINO) [Model Inference](https://analytics-zoo.github.io/master/#ProgrammingGuide/inference/). 

* **High-level ML workflow for automating machine learning tasks**
  - [Cluster Serving](https://analytics-zoo.github.io/master/#ClusterServingGuide/ProgrammingGuide/) for automatically distributed (TensorFlow/PyTorch/Caffe/OpenVINO) model inference . 
  - Scalable [AutoML](https://analytics-zoo.github.io/master/#ProgrammingGuide/AutoML/overview/) for time series prediction.

- **Built-in models** for [Recommendation](https://analytics-zoo.github.io/master/#APIGuide/Models/recommendation/), [Time Series](https://analytics-zoo.github.io/master/#APIGuide/Models/anomaly-detection/), [Computer Vision](https://analytics-zoo.github.io/master/#APIGuide/Models/object-detection/) and [NLP](https://analytics-zoo.github.io/master/#APIGuide/Models/text-matching/) applications.

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##  **Why use Analytics Zoo?**

You may want to develop your AI solutions using Analytics Zoo if:

* You want to easily apply AI models (e.g., TensorFlow, Keras, PyTorch, BigDL, OpenVINO, etc.) to distributed big data.
* You want to transparently scale your AI applications from a single laptop to large clusters with "zero" code changes.
* You want to deploy your AI pipelines to existing YARN or K8S clusters *WITHOUT* any modifications to the clusters.
* You want to automate the process of applying machine learning (such as feature engineering, hyperparameter tuning, model selection, distributed inference, etc.). 

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##  **How to use Analytics Zoo?**

* Check out the [Getting Started page](https://analytics-zoo.github.io/master/#gettingstarted/) for a quick overview of how to use Analytics Zoo.
* Refer to the [Python](https://analytics-zoo.github.io/master/#PythonUserGuide/install/), [Scala]( https://analytics-zoo.github.io/master/#ScalaUserGuide/install/) and [Docker](https://analytics-zoo.github.io/master/#DockerUserGuide/) guides to install Analytics Zoo.
* Visit the [Document Website](https://analytics-zoo.github.io/) ([mirror](https://analytics-zoo.gitee.io/) in China) for more information on Analytics Zoo.
* Check the [Powered By](https://analytics-zoo.github.io/master/#powered-by/) & [Presentations](https://analytics-zoo.github.io/master/#presentations/) pages for real-world applications using Analytics Zoo.
* Join the [Google Group](https://groups.google.com/forum/#!forum/bigdl-user-group) (or subscribe to the [Mail List](mailto:bigdl-user-group+subscribe@googlegroups.com)) for more questions and discussions on Analytics Zoo.