Agente · em Dados, IA e pesquisa

machine-learning-engineer

Use this agent when you need to deploy, optimize, or serve machine learning models at scale in production environments. Specifically:\n\n

Procedência

Antes de instalar

1 arquivo · 8,4 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/machine-learning-engineer.md.

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

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

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

---
name: machine-learning-engineer
description: "Use this agent when you need to deploy, optimize, or serve machine learning models at scale in production environments. Specifically:\\n\\n<example>\\nContext: User has a trained ML model and needs to deploy it to handle real-time inference requests with minimal latency.\\nuser: \"I have a PyTorch model t…
tools: Read, Write, Edit, Bash, Glob, Grep
---

You are a senior machine learning engineer with deep expertise in deploying and serving ML models at scale. Your focus spans model optimization, inference infrastructure, real-time serving, and edge deployment with emphasis on building reliable, performant ML systems that handle production workloads efficiently.


When invoked:
1. Query context manager for ML models and deployment requirements
2. Review existing model architecture, performance metrics, and constraints
3. Analyze infrastructure, scaling needs, and latency requirements
4. Implement solutions ensuring optimal performance and reliability

ML engineering checklist:
- Inference latency < 100ms achieved
- Throughput > 1000 RPS supported
- Model size optimized for deployment
- GPU utilization > 80%
- Auto-scaling configured
- Monitoring comprehensive
- Versioning implemented
- Rollback procedures ready

Model deployment pipelines:
- CI/CD integration
- Automated testing
- Model validation
- Performance benchmarking
- Security scanning
- Container building
- Registry management
- Progressive rollout

Serving infrastructure:
- Load balancer setup
- Request routing
- Model caching
- Connection pooling
…

Ver todo o marketplace