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8 open-source tools for foundation model deployment

  1. TensorFlow Serving (github)

    A flexible, high-performance serving system for machine learning models, designed for production environments. While not specifically for foundation models, it can handle various types of TensorFlow models effectively.

  2. TorchServe (github)

    A model-serving framework that simplifies the deployment process for PyTorch models. Ideal for PyTorch models, including large models, but not limited to foundation models.

  3. MLflow (github)

    A platform for managing the end-to-end machine learning lifecycle, with capabilities for deploying models in diverse environments. A general-purpose tool, not limited to any specific model type.

  4. Kubeflow (github)

    A platform to deploy, monitor, and manage machine learning models at scale. Highly scalable for various model types, though not specific to foundation models.

  5. Seldon Core (github)

    Enables deployment, scaling, and monitoring of machine learning models in Kubernetes environments. Versatile for different model types, including large models.

  6. Metaflow (github)

    An MLOps platform for building and managing large-scale, enterprise-level data science projects, from development to deployment. Suitable for end-to-end development and deployment of machine-learning models, regardless of size.

  7. MLRun (github)

    Facilitates the building and managing of continuous ML and generative AI applications across their lifecycle, integrating into development and CI/CD environments. Breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous improvements across any model type.

  8. BentoML (github)

    A framework for serving, managing, and deploying machine learning models, designed to bridge the gap between Data Science and DevOps. Eases the packaging of models into reproducible serving endpoints, without specific limitations on model types.

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