Simple and Scale AI/ML Workloads: Ray on Vertex AI GA

Scaling AI/ML workloads presents numerous significant issues for developers and engineers. Obtaining the necessary AI infrastructure is one of the challenges

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Google Cloud is pleased to announce that Ray, a powerful distributed Python framework, and Vertex AI have integrated and are currently available

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This integration maximises distributed computing, machine learning, and data processing by letting AI developers scale their workloads on Vertex AI's flexible infrastructure

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Ray's distributed computing platform, which integrates with Vertex AI's infrastructure services, provides a single predictive and generative AI experience

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Vertex AI's robust security architecture may help Ray applications meet business security standards

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Ray on Vertex AI lets you quickly create a Ray cluster using the terminal or Vertex AI SDK for Python before fine-tuning. Gemma

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Using the Vertex AI SDK for Python, you can use Colab Enterprise or any other preferred IDE to connect to the Ray cluster and run your application interactively

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There are many advantages to using Ray on Vertex AI for creating AI/ML applications. In this case, your tuning jobs can be validated by using Vertex AI TensorBoard