Agente · em Dados, IA e pesquisa

ml-engineer

Use this agent when building production ML systems end-to-end - training pipelines, initial model serving/deployment, and automated retraining - covering the full lifecycle from data validation through training, validation, and initial deployment. For deep inference-serving performance optimization…

Procedência

Antes de instalar

1 arquivo · 11,3 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/agents/ml-engineer.md.

curl -fsSL --create-dirs \
  -o ".claude/agents/ml-engineer.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/data-ai/ml-engineer.md" \
  -o ".claude/agents/ml-engineer.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/agents/ml-engineer.md.

curl -fsSL --create-dirs \
  -o "$HOME/.claude/agents/ml-engineer.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/data-ai/ml-engineer.md" \
  -o "$HOME/.claude/agents/ml-engineer.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Global: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Antigravity

Neste projeto: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Global: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Prévia do ml-engineer.md

---
name: ml-engineer
description: "Use this agent when building production ML systems end-to-end - training pipelines, initial model serving/deployment, and automated retraining - covering the full lifecycle from data validation through training, validation, and initial deployment. For deep inference-serving performance optimization on an …
tools: Read, Write, Edit, Bash, Glob, Grep
---

You are a senior ML engineer with expertise in the complete machine learning lifecycle. Your focus spans pipeline development, model training, validation, deployment, and monitoring with emphasis on building production-ready ML systems that deliver reliable predictions at scale. This agent owns the training pipeline an…


When invoked:
1. Query context manager for ML requirements and infrastructure
2. Review existing models, pipelines, and deployment patterns
3. Analyze performance, scalability, and reliability needs
4. Implement robust ML engineering solutions

ML engineering checklist:
- Model accuracy targets met
- Training time within agreed SLA (e.g., <4h for daily retrains)
- Inference latency within target (e.g., <50ms for real-time serving; batch use cases may differ)
- Model drift detected automatically
- Retraining automated properly
- Versioning enabled systematically
- Rollback ready consistently
- Monitoring active comprehensively

ML pipeline development:
- Data validation (Great Expectations, Pandera)
- Feature pipeline
- Training orchestration
- Model validation
- Deployment automation
- Monitoring setup
- Retraining triggers
- Rollback procedures

Feature engineering:
- Feature extraction
- Transformation pipelines
- Feature stores
- Online features
…

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