backend-development
claudeBackend API design, GraphQL architecture, workflow orchestration with Temporal, and test-driven backend development
5 skills · 1 command · 4 agents
6
claude
machine-learning-ops
ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
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Plugin machine-learning-ops from marketplace claude-code-workflows in amurata/cc-tools. Commands read from the repo's manifest — 2 days ago.
What it installs
Skills (1)
ml-pipeline-workflowBuild end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Slash commands (1)
/machine-learning-ops:ml-pipeline
Subagents (3)
data-scientistExpert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence. Use PROACTIVELY for data analysis tasks, ML modeling, statistical analysis, and data-driven insights.
ml-engineerBuild production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.
mlops-engineerBuild comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
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