Overview

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

What is Analytics Zoo?

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

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End-to-end pipeline for applying AI models (TensorFlow, PyTorch, OpenVINO, etc.) to distributed big data
- Write TensorFlow or PyTorch inline with Spark code for distributed training and inference.
- Native deep learning (TensorFlow/Keras/PyTorch/BigDL) support in Spark ML Pipelines.
- Directly run Ray programs on big data cluster through RayOnSpark.
- Plain Java/Python APIs for (TensorFlow/PyTorch/BigDL/OpenVINO) Model Inference.
High-level ML workflow for automating machine learning tasks
Cluster Serving for automatically distributed (TensorFlow/PyTorch/Caffe/OpenVINO) model inference .
Scalable AutoML for time series prediction.
Built-in models for Recommendation, Time Series, Computer Vision and NLP applications.

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.).

Check out the Getting Started page for a quick overview of how to use Analytics Zoo.
Refer to the Python, Scala and Docker guides to install Analytics Zoo.
Visit the Document Website (mirror in China) for more information on Analytics Zoo.
Check the Powered By & Presentations pages for real-world applications using Analytics Zoo.
Join the Google Group (or subscribe to the Mail List) for more questions and discussions on Analytics Zoo.