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MLflow Starter Kit

$39

MLflow experiment tracking setup, model registry patterns, and deployment configs for going from notebooks to production.

📁 8 files🏷 v1.0.0
MarkdownYAMLJSONDockerAWSAzureGCPCI/CDMLflow

📁 File Structure 8 files

mlflow-starter-kit/ ├── LICENSE ├── README.md ├── config.example.yaml ├── docs/ │ ├── checklists/ │ │ └── pre-deployment.md │ ├── overview.md │ └── patterns/ │ └── pattern-01-model-registry-workflow.md └── templates/ └── config.yaml

📖 Documentation Preview README excerpt

MLflow Starter Kit

Production-ready MLflow experiment tracking setup with model registry patterns and deployment configurations. Go from ad-hoc notebook experiments to a structured, reproducible ML workflow.

What's Included

  • MLflow tracking server configuration (local, remote, cloud-hosted)
  • Model registry setup with stage transitions (Staging, Production, Archived)
  • Experiment organization patterns and naming conventions
  • Deployment configs for batch and real-time inference
  • CI/CD integration templates for automated model promotion
  • Docker Compose setup for self-hosted MLflow
  • Cloud deployment guides (AWS, GCP, Azure)

Quick Start


# 1. Copy the example config
cp config.example.yaml config.yaml

# 2. Edit config.yaml with your tracking server details
# 3. Start the MLflow tracking server
docker-compose -f templates/docker-compose.yaml up -d

# 4. Run the example experiment
python examples/train_example.py

Prerequisites

  • Python 3.9+
  • Docker and Docker Compose
  • MLflow 2.x
  • (Optional) Cloud provider CLI for remote deployments

Contents


mlflow-starter-kit/
  config.example.yaml        # Base configuration
  docs/
    overview.md              # Architecture and setup guide
    patterns/
      pattern-01-*.md        # Model registry workflow pattern
    checklists/
      pre-deployment.md      # Go-live checklist
  templates/
    config.yaml              # Production config template

Support

For questions or issues, contact: megafolder122122@hotmail.com

License

MIT License - Copyright 2026 Jesse Mikkola. See LICENSE for details.

📄 Code Sample .yaml preview

config.example.yaml # MLflow Starter Kit - Example Configuration # Copy this file to config.yaml and update values for your environment mlflow: tracking: uri: "http://localhost:5000" # For remote: "https://mlflow.your-domain.com" artifact_location: "./mlruns" # For S3: "s3://your-bucket/mlflow-artifacts" experiment: name: "my-first-experiment" tags: team: "ml-engineering" project: "starter" registry: enabled: true backend_store: "sqlite:///mlflow.db" # For production: "postgresql://user:pass@host:5432/mlflow" server: host: "0.0.0.0" port: 5000 workers: 2 logging: level: "INFO" format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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