Skill · em Dados, IA e pesquisa

mlflow

Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform

Procedência

Antes de instalar

4 arquivos · 63,2 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/skills/mlflow/.

d=".claude/skills/mlflow"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/mlops-mlflow"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/deployment.md" "$u/references/deployment.md" \
  -o "$d/references/model-registry.md" "$u/references/model-registry.md" \
  -o "$d/references/tracking.md" "$u/references/tracking.md"

Global: instala em ~/.claude/skills/mlflow/.

d="$HOME/.claude/skills/mlflow"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/mlops-mlflow"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/deployment.md" "$u/references/deployment.md" \
  -o "$d/references/model-registry.md" "$u/references/model-registry.md" \
  -o "$d/references/tracking.md" "$u/references/tracking.md"

Codex

Neste projeto: instala em .agents/skills/mlflow/.

d=".agents/skills/mlflow"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/mlops-mlflow"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/deployment.md" "$u/references/deployment.md" \
  -o "$d/references/model-registry.md" "$u/references/model-registry.md" \
  -o "$d/references/tracking.md" "$u/references/tracking.md"

Global: instala em ~/.agents/skills/mlflow/.

d="$HOME/.agents/skills/mlflow"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/mlops-mlflow"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/deployment.md" "$u/references/deployment.md" \
  -o "$d/references/model-registry.md" "$u/references/model-registry.md" \
  -o "$d/references/tracking.md" "$u/references/tracking.md"

Antigravity

Neste projeto: instala em .agents/skills/mlflow/.

d=".agents/skills/mlflow"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/mlops-mlflow"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/deployment.md" "$u/references/deployment.md" \
  -o "$d/references/model-registry.md" "$u/references/model-registry.md" \
  -o "$d/references/tracking.md" "$u/references/tracking.md"

Global: instala em ~/.gemini/antigravity-cli/skills/mlflow/.

d="$HOME/.gemini/antigravity-cli/skills/mlflow"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/mlops-mlflow"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/deployment.md" "$u/references/deployment.md" \
  -o "$d/references/model-registry.md" "$u/references/model-registry.md" \
  -o "$d/references/tracking.md" "$u/references/tracking.md"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.mlflow

Prévia do SKILL.md

---
name: mlflow
description: Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [MLOps, MLflow, Experiment Tracking, Model Registry, ML Lifecycle, Deployment, Model Versioning, PyTorch, TensorFlow, Scikit-Learn, HuggingFace]
dependencies: [mlflow, sqlalchemy, boto3]
---

# MLflow: ML Lifecycle Management Platform

## When to Use This Skill

Use MLflow when you need to:
- **Track ML experiments** with parameters, metrics, and artifacts
- **Manage model registry** with versioning and stage transitions
- **Deploy models** to various platforms (local, cloud, serving)
- **Reproduce experiments** with project configurations
- **Compare model versions** and performance metrics
- **Collaborate** on ML projects with team workflows
- **Integrate** with any ML framework (framework-agnostic)

**Users**: 20,000+ organizations | **GitHub Stars**: 23k+ | **License**: Apache 2.0

## Installation

```bash
# Install MLflow
pip install mlflow

# Install with extras
pip install mlflow[extras]  # Includes SQLAlchemy, boto3, etc.

# Start MLflow UI
mlflow ui

# Access at http://localhost:5000
```
…

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