Showing 5 open source projects for "apache local server"

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    Go From AI Idea to AI App Fast

    One platform to build, fine-tune, and deploy ML models. No MLOps team required.

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  • 1
    Nuclio

    Nuclio

    High-Performance Serverless event and data processing platform

    Nuclio is an open source and managed serverless platform used to minimize development and maintenance overhead and automate the deployment of data-science-based applications. Real-time performance running up to 400,000 function invocations per second. Portable across low laptops, edge, on-prem and multi-cloud deployments. The first serverless platform supporting GPUs for optimized utilization and sharing. Automated deployment to production in a few clicks from Jupyter notebook. Deploy one of...
    Downloads: 0 This Week
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  • 2
    Milvus

    Milvus

    Vector database for scalable similarity search and AI applications

    Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility. Average latency measured in milliseconds on trillion...
    Downloads: 1 This Week
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  • 3
    ClearML

    ClearML

    Streamline your ML workflow

    ClearML is an open source platform that automates and simplifies developing and managing machine learning solutions for thousands of data science teams all over the world. It is designed as an end-to-end MLOps suite allowing you to focus on developing your ML code & automation, while ClearML ensures your work is reproducible and scalable. The ClearML Python Package for integrating ClearML into your existing scripts by adding just two lines of code, and optionally extending your experiments...
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  • 4
    SageMaker Inference Toolkit

    SageMaker Inference Toolkit

    Serve machine learning models within a Docker container

    Serve machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. Once you have a trained model, you can include it in a Docker container that runs your inference code. A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the...
    Downloads: 0 This Week
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  • Forever Free Full-Stack Observability | Grafana Cloud Icon
    Forever Free Full-Stack Observability | Grafana Cloud

    Our generous forever free tier includes the full platform, including the AI Assistant, for 3 users with 10k metrics, 50GB logs, and 50GB traces.

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  • 5
    Seldon Server

    Seldon Server

    Machine learning platform and recommendation engine on Kubernetes

    Seldon Server is a machine learning platform and recommendation engine built on Kubernetes. Seldon reduces time-to-value so models can get to work faster. Scale with confidence and minimize risk through interpretable results and transparent model performance. Seldon Core focuses purely on deploying a wide range of ML models on Kubernetes, allowing complex runtime serving graphs to be managed in production. Seldon Core is a progression of the goals of the Seldon-Server project but also a more...
    Downloads: 0 This Week
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