Machine learning models need to be put to work in the real world. This skill helps you move them into production. It builds the systems that serve predictions fast and reliably.
It handles model optimization to run faster and use less memory. It sets up auto-scaling to handle traffic spikes. It monitors performance to catch problems early.
You get tools for batch predictions, edge devices, and A/B testing. The goal is to make ML models work well at any scale.
Global
mkdir -p ~/.claude/skills/machine-learning-engineerProject
mkdir -p .claude/skills/machine-learning-engineerExplore Runlllllllama/rigorpilot-skills
Plan and rank quick deep learning exploration runs with clear boundaries
Run Trainlllllllama/rigorpilot-skills
Run deep learning training conservatively with structured logs and metrics
Env And Assets Bootstraplllllllama/rigorpilot-skills
Safe and careful setup of conda environments and asset paths for deep learning reproduction
Env And Assets Bootstraplllllllama/ai-paper-reproduction-skill
Prepare conda-first environment and asset paths for deep learning reproduction
Data Visualizationanthropics/knowledge-work-plugins
Pick the right chart, write clean Python code, and design for everyone
Powerbi Modelinggithub/awesome-copilot
Expert guidance for building optimized Power BI semantic models with star schemas and DAX
Power Bi Dax Optimizationgithub/awesome-copilot
Make your Power BI DAX formulas faster and easier to maintain
Bigquery Basicsgoogle/skills
Easily analyze massive datasets with SQL and built-in machine learning