• Easily Host LLMs and Web Apps on Cloud Run Icon
    Easily Host LLMs and Web Apps on Cloud Run

    Run everything from popular models with on-demand NVIDIA L4 GPUs to web apps without infrastructure management.

    Run frontend and backend services, batch jobs, host LLMs, and queue processing workloads without the need to manage infrastructure. Cloud Run gives you on-demand GPU access for hosting LLMs and running real-time AI—with 5-second cold starts and automatic scale-to-zero so you only pay for actual usage. New customers get $300 in free credit to start.
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  • Build AI Apps with Gemini 3 on Vertex AI Icon
    Build AI Apps with Gemini 3 on Vertex AI

    Access Google’s most capable multimodal models. Train, test, and deploy AI with 200+ foundation models on one platform.

    Vertex AI gives developers access to Gemini 3—Google’s most advanced reasoning and coding model—plus 200+ foundation models including Claude, Llama, and Gemma. Build generative AI apps with Vertex AI Studio, customize with fine-tuning, and deploy to production with enterprise-grade MLOps. New customers get $300 in free credits.
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  • 1
    Substra

    Substra

    Low-level Python library used to interact with a Substra network

    An open-source framework supporting privacy-preserving, traceable federated learning and machine learning orchestration. Offers a Python SDK, high-level FL library (SubstraFL), and web UI to define datasets, models, tasks, and orchestrate secure, auditable collaborations.
    Downloads: 0 This Week
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  • 2
    Xfl

    Xfl

    An Efficient and Easy-to-use Federated Learning Framework

    XFL is a lightweight, high-performance federated learning framework supporting both horizontal and vertical FL. It integrates homomorphic encryption, DP, secure MPC, and optimizes network resilience. Compatible with major ML libraries and deployable via Docker or Conda.
    Downloads: 1 This Week
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  • 3
    Appfl

    Appfl

    Advanced Privacy-Preserving Federated Learning framework

    APPFL (Advanced Privacy-Preserving Federated Learning) is a Python framework enabling researchers to easily build and benchmark privacy-aware federated learning solutions. It supports flexible algorithm development, differential privacy, secure communications, and runs efficiently on HPC and multi-GPU setups.
    Downloads: 0 This Week
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  • 4
    NVIDIA FLARE

    NVIDIA FLARE

    NVIDIA Federated Learning Application Runtime Environment

    NVIDIA Federated Learning Application Runtime Environment NVIDIA FLARE is a domain-agnostic, open-source, extensible SDK that allows researchers and data scientists to adapt existing ML/DL workflows(PyTorch, TensorFlow, Scikit-learn, XGBoost etc.) to a federated paradigm. It enables platform developers to build a secure, privacy-preserving offering for a distributed multi-party collaboration. NVIDIA FLARE is built on a componentized architecture that allows you to take federated learning workloads from research and simulation to real-world production deployment.
    Downloads: 1 This Week
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  • Cut Cloud Costs with Google Compute Engine Icon
    Cut Cloud Costs with Google Compute Engine

    Save up to 91% with Spot VMs and get automatic sustained-use discounts. One free VM per month, plus $300 in credits.

    Save on compute costs with Compute Engine. Reduce your batch jobs and workload bill 60-91% with Spot VMs. Compute Engine's committed use offers customers up to 70% savings through sustained use discounts. Plus, you get one free e2-micro VM monthly and $300 credit to start.
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  • 5
    FATE

    FATE

    An industrial grade federated learning framework

    FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). Supporting various federated learning scenarios, FATE now provides a host of federated learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning. FATE became open-source in February 2019. FATE TSC was established to lead FATE open-source community, with members from major domestic cloud computing and financial service enterprises. ...
    Downloads: 0 This Week
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