MLflow Patterns
ML experiment tracking, model registry, and deployment with MLflow for reproducible machine learning workflows.
npx skills add majiayu000/claude-skill-registry --skill mlflow-patterns --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# MLflow Patterns ## Overview MLflow เป็น open-source platform สำหรับ managing ML lifecycle ครอบคลุม experiment tracking, model packaging, model registry, และ deployment ช่วยให้ทีม data science ทำงานร่วมกันและ deploy models ได้อย่าง reproducible ## Why This Matters - **Reproducibility**: Track experiments และ reproduce results - **Collaboration**: Share experiments และ models across team - **Deployment**: Package และ deploy models consistently - **Governance**: Model versioning และ approval workflow --- ## Core Concepts ### 1. Experiment Tracking ```python import mlflow from mlflow.tracking import MlflowClient # Set tracking URI mlflow.set_tracking_uri("http://mlflow-server:5000") mlflow.set_experiment("customer-churn-prediction") # Start run with auto-logging mlflow.sklearn.autolog() with mlflow.start_run(run_name="xgboost-v1") as run: # Log parameters mlflow.log_params({ "learning_rate": 0.1, "max_depth": 6, "n_estimators": 100, "subsample": 0.8, }) # Train model model = XGBClassifier( learning_rate=0.1, max_depth=6, n_estimators=100, subsample=0.8, ) model.fit(X_train, y_train) # Log metrics y_pred = model.predict(X_test) mlflow.log_metrics({ "accuracy": accuracy_score(y_test, y
- Overview
- Why This Matters
- Core Concepts
- 1. Experiment Tracking
- 2. Custom Model Wrapper
- 3. Model Registry
- 4. Model Validation Pipeline
- 5. Model Serving
- Quick Start
- Production Checklist
- Anti-patterns
- Integration Points
- Further Reading
pip install mlflow mlflow server --backend-store-uri sqlite:///mlflow.db \
What does the MLflow Patterns skill do?
ML experiment tracking, model registry, and deployment with MLflow for reproducible machine learning workflows.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill mlflow-patterns --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.