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  • 1
    Mosec

    Mosec

    A high-performance ML model serving framework, offers dynamic batching

    Mosec is a high-performance and flexible model-serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API.
    Downloads: 0 This Week
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    aqueduct LLM

    aqueduct LLM

    Aqueduct allows you to run LLM and ML workloads on any infrastructure

    Aqueduct is an MLOps framework that allows you to define and deploy machine learning and LLM workloads on any cloud infrastructure. Aqueduct is an open-source MLOps framework that allows you to write code in vanilla Python, run that code on any cloud infrastructure you'd like to use, and gain visibility into the execution and performance of your models and predictions. Aqueduct's Python native API allows you to define ML tasks in regular Python code. You can connect Aqueduct to your existing...
    Downloads: 0 This Week
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  • 3
    KubeAI

    KubeAI

    Private Open AI on Kubernetes

    Get inferencing running on Kubernetes: LLMs, Embeddings, Speech-to-Text. KubeAI serves an OpenAI compatible HTTP API. Admins can configure ML models by using the Model Kubernetes Custom Resources. KubeAI can be thought of as a Model Operator (See Operator Pattern) that manages vLLM and Ollama servers.
    Downloads: 0 This Week
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