mlflow
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform
npx skills add Orchestra-Research/AI-Research-SKILLs --skill mlflow --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: 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 ``` ## Quick Start ### Basic Tracking ```python import mlflow # Start a run with mlflow.start_run(): # Log parameters mlflow.log_param("learning_rate", 0.001) mlflow.log_param("batch_size", 32) # Your training code model = train_model() # Log metrics mlflow.log_metric("train_loss", 0.15) mlflow.log_metric("val_accuracy", 0.92) # Log model mlflow.sklearn.log_model(model, "model") ``` ### Aut
- When to Use This Skill
- Installation
- Quick Start
- Basic Tracking
- Autologging (Automatic Tracking)
- Core Concepts
- 1. Experiments and Runs
- 2. Logging Parameters
- 3. Logging Metrics
- 4. Logging Artifacts
- 5. Logging Models
- Autologging
- Enable Autologging
- Autologging with Scikit-learn
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 Serve registered model mlflow models serve -m "models:/my-classifier/Production" -p 5001 Serve from run
What does the mlflow skill do?
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill mlflow --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 Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 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.