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  2. Kubeflow - Wikipedia

    en.wikipedia.org/wiki/Kubeflow

    Kubeflow is an open-source platform for machine learning and MLOps on Kubernetes introduced by Google.The different stages in a typical machine learning lifecycle are represented with different software components in Kubeflow, including model development (Kubeflow Notebooks [4]), model training (Kubeflow Pipelines, [5] Kubeflow Training Operator [6]), model serving (KServe [a] [7]), and ...

  3. Google Cloud Platform - Wikipedia

    en.wikipedia.org/wiki/Google_Cloud_Platform

    Google Cloud Platform is a part [8] of Google Cloud, which includes the Google Cloud Platform public cloud infrastructure, as well as Google Workspace (G Suite), enterprise versions of Android and ChromeOS, and application programming interfaces (APIs) for machine learning and enterprise mapping services.

  4. fast.ai - Wikipedia

    en.wikipedia.org/wiki/Fast.ai

    In the fall of 2018, fast.ai released v1.0 of their free open-source library for deep learning called fastai (without a period), sitting atop PyTorch. Google Cloud was the first to announce its support. [6] This open-source framework is hosted on GitHub and is licensed under the Apache License, Version 2.0. [7] [8]

  5. Here are the winners of the Google Cloud machine learning ...

    www.aol.com/news/winners-google-cloud-machine...

    Back in March at Google's Cloud Next conference, the company announced plans to run its own machine learning startup competition side-by-side with Data Collective and Emergence Capital. Four ...

  6. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    In May 2016, Google announced its Tensor processing unit (TPU), an application-specific integrated circuit (ASIC, a hardware chip) built specifically for machine learning and tailored for TensorFlow. A TPU is a programmable AI accelerator designed to provide high throughput of low-precision arithmetic (e.g., 8-bit ), and oriented toward using ...

  7. Google JAX - Wikipedia

    en.wikipedia.org/wiki/Google_JAX

    JAX is a machine learning framework for transforming numerical functions. [2] [3] [4] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).

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