4 repository-uri
Awesome GitHub RepositoriesML Orchestration Deployments
Methods and configurations for deploying machine learning services using container orchestration platforms.
Distinguishing note: Focuses on the deployment of ML-specific services into orchestration environments, distinct from general infrastructure management.
Explore 4 awesome GitHub repositories matching devops & infrastructure · ML Orchestration Deployments. Refine with filters or upvote what's useful.
Awesome ML Orchestration Deployments GitHub Repositories
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- mlflow/mlflow
mlflow/mlflow
26,554Vezi pe GitHubDeploy the tracking server using container orchestration tools or managed cloud services for production-scale environments.
Pythonagentopsagentsai - voltagent/awesome-claude-code-subagents
VoltAgent/awesome-claude-code-subagents
21,906Vezi pe GitHubThis project provides a framework for managing multi-agent systems, designed to automate complex software development, infrastructure, and business workflows. It functions as a multi-agent workflow orchestrator that routes tasks to domain-specific workers while maintaining state persistence and infrastructure automation. By leveraging large language models, the system decomposes high-level objectives into actionable plans, ensuring that complex operations are executed with consistency and reliability. The framework distinguishes itself through its hierarchical agent registry and policy-driven
Deploys infrastructure and CI/CD pipelines to build reproducible machine learning environments.
Shellai-agent-frameworkai-agent-toolsai-agents - cortexlabs/cortex
cortexlabs/cortex
8,013Vezi pe GitHubCortex is a Kubernetes-based machine learning infrastructure platform designed for deploying, scaling, and managing models and workloads. It functions as a serverless inference engine and GPU cluster orchestrator, providing the tools necessary to execute real-time, asynchronous, and batch model predictions. The platform utilizes declarative infrastructure-as-code for provisioning model clusters and environments. It optimizes operational costs by elastically scaling CPU and GPU resources through the use of spot instances. The system covers a broad set of operational capabilities, including wo
Orchestrates the deployment and scaling of machine learning models across production infrastructure to handle traffic loads.
Goinfrastructuremachine-learning - h2oai/h2o-3
h2oai/h2o-3
7,493Vezi pe GitHubh2o-3 is a distributed machine learning platform and automated machine learning framework designed for training and deploying predictive models using distributed in-memory computing. It functions as a deep learning framework and a distributed model scoring engine, capable of operating as a Kubernetes ML cluster to process large datasets in parallel. The platform distinguishes itself through automated machine learning capabilities that automatically select the best algorithms and hyperparameters to optimize model performance. It provides specialized deep learning toolkits for tasks including i
Deploys machine learning services on orchestration platforms using charts for runtime compatibility and configuration.
Jupyter Notebookautomlbig-datadata-science
