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open-source MLOps platform
The main features of infuseai/primehub are: MLOps Platforms.
Projects with overlapping indexed features include: bentoml/openllm — OpenLLM is a framework for deploying, managing, and scaling open-source large language models. dstack-tee/dstack — Open framework for confidential AI. kserve/kserve — KServe is a Kubernetes-native platform for deploying and serving machine learning models as scalable inference… kubeflow/kubeflow — Kubeflow is a Kubernetes machine learning platform and containerized toolkit designed to orchestrate the entire… logicalclocks/hopsworks — Hopsworks - Data-Intensive AI platform with a Feature Store. microsoft/pai — Resource scheduling and cluster management for AI.
KServe is a Kubernetes-native platform for deploying and serving machine learning models as scalable inference services. It supports both generative AI models, including large language models, and traditional predictive models from frameworks such as TensorFlow, PyTorch, Scikit-Learn, XGBoost, and ONNX. The platform manages the full lifecycle of model deployments, including revision tracking, canary rollouts, A/B testing, and automatic rollbacks, and provides serverless scale-to-zero capabilities for cost-efficient resource management. KServe distinguishes itself through a standardized infere
Kubeflow is a Kubernetes machine learning platform and containerized toolkit designed to orchestrate the entire machine learning lifecycle. It functions as an MLOps workflow orchestrator and infrastructure layer for building, training, and deploying models within containerized environments. The project provides specialized infrastructure for scaling compute resources and managing GPU workloads for large-scale distributed training. It automates the transition of models from experimental development to production through workflow orchestration and model deployment services. The platform covers
OpenLLM is a framework for deploying, managing, and scaling open-source large language models
