As part of our blog series on Ray, this post analyzes the ecosystem that Ray has built around its platform. If you missed our first blog post on Ray, you might read it first.
Ray is a relatively young open source project, created in 2016 as part of the research project from UC Berkeley. Nevertheless, Ray has created an impressive ecosystem around its platform. The graph below shows the links between Ray and key projects in its ecosystem.
|Machine Learning Area||Projects|
|Feature Engineering||Dask, Mars, Modin, RayDP, Spark|
|Natural Language Processing (NLP)||Hugging Face Transformers, spaCy|
|Core Machine Learning||PyTorch, Scikit-learn, TensorFlow|
|Data Representation||NumPy, Pandas|
|End-to-End ML||Analytics Zoo, Flambe, PyCaret|
Table 1 above shows the integrations organized by their application area. Not only does Ray integrate with the core ML frameworks, but it also has integrations with each step in the ML pipeline. Ray has become the center of an interoperable ecosystem for distributed machine learning.