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projects:
  - name: Classy Vision Integration
    section_title: Classy Vision
    description: Classy Vision is a new end-to-end, PyTorch-based framework for
      large-scale training of state-of-the-art image and video classification models.
      The library features a modular, flexible design that allows anyone to train
      machine learning models on top of PyTorch using very simple abstractions.
    website: https://github.com/facebookresearch/ClassyVision/blob/main/tutorials/ray_aws.ipynb
    repo: https://github.com/facebookresearch/ClassyVision
    image: ../images/classyvision.png
  - name: Dask Integration
    section_title: Dask
    description: Dask provides advanced parallelism for analytics, enabling performance
      at scale for the tools you love. Dask uses existing Python APIs and data
      structures to make it easy to switch between Numpy, Pandas,
      Scikit-learn to their Dask-powered equivalents.
    website: dask-on-ray
    repo: https://github.com/dask/dask
    image: ../images/dask.png
  - name: Flambé Integration
    section_title: Flambé
    description: Flambé is a machine learning experimentation framework built to
      accelerate the entire research life cycle. Flambé’s main objective is to
      provide a unified interface for prototyping models, running experiments
      containing complex pipelines, monitoring those experiments in real-time,
      reporting results, and deploying a final model for inference.
    website: https://github.com/asappresearch/flambe
    repo: https://github.com/asappresearch/flambe
    image: ../images/flambe.png
  - name: Flyte Integration
    section_title: Flyte
    description: Flyte is a Kubernetes-native workflow automation platform for complex,
      mission-critical data and ML processes at scale. It has been battle-tested
      at Lyft, Spotify, Freenome, and others and is truly open-source.
    website: https://flyte.org/
    repo: https://github.com/flyteorg/flyte
    image: ../images/flyte.png
  - name: Horovod Integration
    section_title: Horovod
    description: Horovod is a distributed deep learning training framework for
      TensorFlow, Keras, PyTorch, and Apache MXNet. The goal of
      Horovod is to make distributed deep learning fast and easy to use.
    website: https://horovod.readthedocs.io/en/stable/ray_include.html
    repo: https://github.com/horovod/horovod
    image: ../images/horovod.png
  - name: Hugging Face Transformers Integration
    section_title: Hugging Face Transformers
    description: State-of-the-art Natural Language Processing for
      Pytorch and TensorFlow 2.0. It integrates with Ray for distributed
      hyperparameter tuning of transformer models.
    website: https://huggingface.co/transformers/master/main_classes/trainer.html#transformers.Trainer.hyperparameter_search
    repo: https://github.com/huggingface/transformers
    image: ../images/hugging.png
  - name: Intel Analytics Zoo Integration
    section_title: Intel Analytics Zoo
    description: Analytics Zoo seamlessly scales TensorFlow, Keras and PyTorch
      to distributed big data (using Spark, Flink & Ray).
    website: https://analytics-zoo.github.io/master/#ProgrammingGuide/rayonspark/
    repo: https://github.com/intel-analytics/analytics-zoo
    image: ../images/zoo.png
  - name: NLU Integration
    section_title: John Snow Labs' NLU
    description: The power of 350+ pre-trained NLP models, 100+ Word Embeddings,
      50+ Sentence Embeddings, and 50+ Classifiers in 46 languages
      with 1 line of Python code.
    website: https://nlu.johnsnowlabs.com/docs/en/predict_api#modin-dataframe
    repo: https://github.com/JohnSnowLabs/nlu
    image: ../images/nlu.png
  - name: Ludwig Integration
    section_title: Ludwig AI
    description: Ludwig is a toolbox that allows users to train and test deep learning
      models without the need to write code. With Ludwig, you can train a deep learning
      model on Ray in zero lines of code, automatically leveraging Dask on Ray for data
      preprocessing, Horovod on Ray for distributed training, and Ray Tune for
      hyperparameter optimization.
    website: https://medium.com/ludwig-ai/ludwig-ai-v0-4-introducing-declarative-mlops-with-ray-dask-tabnet-and-mlflow-integrations-6509c3875c2e
    repo: https://github.com/ludwig-ai/ludwig
    image: ../images/ludwig.png
  - name: MARS Integration
    section_title: MARS
    description: Mars is a tensor-based unified framework for large-scale data
      computation which scales Numpy, Pandas and Scikit-learn. Mars can scale in to
      a single machine, and scale out to a cluster with thousands of machines.
    website: mars-on-ray
    repo: https://github.com/mars-project/mars
    image: ../images/mars.png
  - name: Modin Integration
    section_title: Modin
    description: Scale your pandas workflows by changing one line of code.
      Modin transparently distributes the data and computation so that all you need
      to do is continue using the pandas API as you were before installing Modin.
    website: https://github.com/modin-project/modin
    repo: https://github.com/modin-project/modin
    image: ../images/modin.png
  - name: Prefect Integration
    section_title: Prefect
    description: Prefect is an open source workflow orchestration platform in Python.
      It allows you to easily define, track and schedule workflows in Python. This
      integration makes it easy to run a Prefect workflow on a Ray cluster in a
      distributed way.
    website: https://github.com/PrefectHQ/prefect-ray
    repo: https://github.com/PrefectHQ/prefect-ray
    image: ../images/prefect.png
  - name: PyCaret Integration
    section_title: PyCaret
    description: PyCaret is an open source low-code machine learning library in Python
      that aims to reduce the hypothesis to insights cycle time in a ML experiment.
      It enables data scientists to perform end-to-end experiments quickly
      and efficiently.
    website: https://github.com/pycaret/pycaret
    repo: https://github.com/pycaret/pycaret
    image: ../images/pycaret.png
  - name: PyTorch Lightning Integration
    section_title: PyTorch Lightning
    description: PyTorch Lightning is a popular open-source library that provides a
      high level interface for PyTorch. The goal of PyTorch Lightning is to structure
      your PyTorch code to abstract the details of training, making AI research
      scalable and fast to iterate on.
    website: https://github.com/ray-project/ray_lightning_accelerators
    repo: https://github.com/ray-project/ray_lightning_accelerators
    image: ../images/pytorch_lightning_small.png
  - name: RayDP Integration
    section_title: Spark on Ray (RayDP)
    description: RayDP ("Spark on Ray") enables you to easily use Spark inside a
      Ray program. You can use Spark to read the input data, process the data using
      SQL, Spark DataFrame, or Pandas (via Koalas) API, extract and transform features
      using Spark MLLib, and use RayDP Estimator API for distributed training
      on the preprocessed dataset.
    website: https://github.com/Intel-bigdata/oap-raydp
    repo: https://github.com/Intel-bigdata/oap-raydp
    image: ../images/intel.png
  - name: Scikit Learn Integration
    section_title: Scikit Learn
    description: Scikit-learn is a free software machine learning library for
      the Python programming language. It features various classification,
      regression and clustering algorithms including support vector machines,
      random forests, gradient boosting, k-means and DBSCAN, and is designed to
      interoperate with the Python numerical and scientific libraries NumPy and SciPy.
    website: https://docs.ray.io/en/master/joblib.html
    repo: https://github.com/scikit-learn/scikit-learn
    image: ../images/scikit.png
  - name: Seldon Alibi Integration
    section_title: Seldon Alibi
    description: Alibi is an open source Python library aimed at machine learning model
      inspection and interpretation. The focus of the library is to provide high-quality
      implementations of black-box, white-box, local and global explanation methods for
      classification and regression models.
    website: https://github.com/SeldonIO/alibi
    repo: https://github.com/SeldonIO/alibi
    image: ../images/seldon.png
  - name: Sematic Integration
    section_title: Sematic
    description: Sematic is an open-source ML pipelining tool written in Python.
      It enables users to write end-to-end pipelines that can seamlessly transition between
      your laptop and the cloud, with rich visualizations, traceability,
      reproducibility, and usability as first-class citizens. This integration
      enables dynamic allocation of Ray clusters within Sematic pipelines.
    website: https://docs.sematic.dev/integrations/ray
    repo: https://github.com/sematic-ai/sematic
    image: ../images/sematic.png
  - name: spaCy Integration
    section_title: spaCy
    description: spaCy is a library for advanced Natural Language Processing in Python
      and Cython. It's built on the very latest research, and was designed from
      day one to be used in real products.
    website: https://pypi.org/project/spacy-ray/
    repo: https://github.com/explosion/spacy-ray
    image: ../images/spacy.png
  - name: XGBoost Integration
    section_title: XGBoost
    description: XGBoost is a popular gradient boosting library for classification
      and regression. It is one of the most popular tools in data science and
      workhorse of many top-performing Kaggle kernels.
    website: https://github.com/ray-project/xgboost_ray
    repo: https://github.com/ray-project/xgboost_ray
    image: ../images/xgboost_logo.png
  - name: LightGBM Integration
    section_title: LightGBM
    description: LightGBM is a high-performance gradient boosting library for
      classification and regression. It is designed to be distributed and efficient.
    website: https://github.com/ray-project/lightgbm_ray
    repo: https://github.com/ray-project/lightgbm_ray
    image: ../images/lightgbm_logo.png
  - name: Volcano Integration
    section_title: Volcano
    description: Volcano is system for running high-performance workloads 
      on Kubernetes. It features powerful batch scheduling capabilities required by ML
      and other data-intensive workloads.
    website: https://github.com/volcano-sh/volcano/releases/tag/v1.7.0
    repo: https://github.com/volcano-sh/volcano/
    image: ./images/volcano.png