Summary
This covers the production ML engineering stack you'd actually need: deployment workflows with Docker and serving options like Triton or Ray Serve, MLOps pipelines with feature stores and experiment tracking, and RAG system implementation with vector database comparisons. The monitoring section gives you drift detection code and alert thresholds that aren't just theoretical. What's useful here is the decision matrices, like when to use which chunking strategy or which LLM provider based on cost per token. It assumes you already know ML fundamentals and need the infrastructure patterns to ship models rather than just train them. The canary deployment workflow and retraining triggers are especially practical if you're moving from notebooks to production.
Install to Claude Code
npx -y skills add borghei/claude-skills --skill senior-ml-engineer --agent claude-codeInstalls into .claude/skills of the current project.
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Files
SKILL.md
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Plug Mailtrap into your AI workflow and let it handle the email.
Connect Mailtrap MCP →Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →Make coding agent sessions - Searchable, Shareable, Vendor-neutral & Scored.
Try For Free →Your agent targets a perfect 10 Code Health score. Deterministic. Every commit.
Try For Free →Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →belt cli automatically finds the best tools and skills for your agent. image, video, music, tts...
one prompt install →
