wehub-resource-sync
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3.4 KiB
ReStructuredText
64 lines
3.4 KiB
ReStructuredText
.. _ray-for-ml-infra:
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Ray for ML Infrastructure
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=========================
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.. tip::
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We'd love to hear from you if you are using Ray to build an ML platform! Fill out `this short form <https://forms.gle/wCCdbaQDtgErYycT6>`__ to get involved.
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Ray and its AI libraries provide a unified compute runtime for teams looking to simplify their ML platform.
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Ray's libraries such as Ray Train, Ray Data, and Ray Serve can be used to compose end-to-end ML workflows, providing features and APIs for
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data preprocessing as part of training, and transitioning from training to serving.
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..
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https://docs.google.com/drawings/d/1PFA0uJTq7SDKxzd7RHzjb5Sz3o1WvP13abEJbD0HXTE/edit
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.. image:: /images/ray-air.svg
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Why Ray for ML Infrastructure?
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------------------------------
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Ray's AI libraries simplify the ecosystem of machine learning frameworks, platforms, and tools, by providing a seamless, unified, and open experience for scalable ML:
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.. image:: images/why-air-2.svg
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..
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https://docs.google.com/drawings/d/1oi_JwNHXVgtR_9iTdbecquesUd4hOk0dWgHaTaFj6gk/edit
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**1. Seamless Dev to Prod**: Ray's AI libraries reduce friction going from development to production. With Ray and its libraries, the same Python code scales seamlessly from a laptop to a large cluster.
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**2. Unified ML API and Runtime**: Ray's APIs enable swapping between popular frameworks, such as XGBoost, PyTorch, and Hugging Face, with minimal code changes. Everything from training to serving runs on a single runtime (Ray + KubeRay).
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**3. Open and Extensible**: Ray is fully open-source and can run on any cluster, cloud, or Kubernetes. Build custom components and integrations on top of scalable developer APIs.
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Example ML Platforms built on Ray
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---------------------------------
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`Merlin <https://shopify.engineering/merlin-shopify-machine-learning-platform>`_ is Shopify's ML platform built on Ray. It enables fast-iteration and `scaling of distributed applications <https://www.youtube.com/watch?v=kbvzvdKH7bc>`_ such as product categorization and recommendations.
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.. figure:: /images/shopify-workload.png
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Shopify's Merlin architecture built on Ray.
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Spotify `uses Ray for advanced applications <https://engineering.atspotify.com/2023/02/unleashing-ml-innovation-at-spotify-with-ray/>`_ that include personalizing content recommendations for home podcasts, and personalizing Spotify Radio track sequencing.
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.. figure:: /images/spotify.png
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How Ray ecosystem empowers ML scientists and engineers at Spotify.
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The following highlights feature companies leveraging Ray's unified API to build simpler, more flexible ML platforms.
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- `[Blog] The Magic of Merlin - Shopify's New ML Platform <https://shopify.engineering/merlin-shopify-machine-learning-platform>`_
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- `[Slides] Large Scale Deep Learning Training and Tuning with Ray <https://drive.google.com/file/d/1BS5lfXfuG5bnI8UM6FdUrR7CiSuWqdLn/view>`_
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- `[Blog] Griffin: How Instacart’s ML Platform Tripled in a year <https://www.instacart.com/company/how-its-made/griffin-how-instacarts-ml-platform-tripled-ml-applications-in-a-year/>`_
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- `[Talk] Predibase - A low-code deep learning platform built for scale <https://www.youtube.com/watch?v=B5v9B5VSI7Q>`_
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- `[Blog] Building a ML Platform with Kubeflow and Ray on GKE <https://cloud.google.com/blog/products/ai-machine-learning/build-a-ml-platform-with-kubeflow-and-ray-on-gke>`_
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- `[Talk] Ray Summit Panel - ML Platform on Ray <https://www.youtube.com/watch?v=_L0lsShbKaY>`_
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.. Deployments on Ray.
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.. include:: /ray-air/deployment.rst
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