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ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
npx claudepluginhub ai-foundry-core/ril-agents --plugin machine-learning-opsExpert 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.
Build 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.
Build 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.
Uses power tools
Uses Bash, Write, or Edit tools
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Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
Deploy ML models to production
ML engineering plugin: Give your AI coding agent ML engineering superpowers.
AI/ML development: LLM architecture, prompt engineering, ML ops, and NLP with production deployment focus
ML engineering agents providing expertise in MLOps, model deployment, and inference optimization
ML experiment tracking with metrics logging and run comparison
Test-driven development methodology with red-green-refactor cycles and code review
Code cleanup, refactoring automation, and technical debt management with context restoration
Multi-agent system optimization, agent improvement workflows, and context management
Pre-deployment checks, configuration validation, and deployment readiness assessment
Team workflows, issue management, standup automation, and developer experience optimization
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