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ML Data Versioning

$29

DVC setup, data pipeline versioning, experiment reproducibility, and artifact management workflows.

📁 8 files🏷 v1.0.0
MarkdownYAMLJSONAzureCI/CD

📁 File Structure 8 files

ml-data-versioning/ ├── LICENSE ├── README.md ├── config.example.yaml ├── docs/ │ ├── checklists/ │ │ └── pre-deployment.md │ ├── overview.md │ └── patterns/ │ └── pattern-01-data-pipeline-versioning.md └── templates/ └── config.yaml

📖 Documentation Preview README excerpt

ML Data Versioning

DVC-based data versioning setup with data pipeline versioning, experiment reproducibility patterns, and artifact management. Track and version your datasets alongside your code.

What's Included

  • DVC setup and configuration for data versioning
  • Data pipeline definition and versioning templates
  • Experiment reproducibility workflows
  • Artifact management with remote storage backends
  • Git integration patterns for data+code versioning
  • Migration guides from ad-hoc to versioned data workflows
  • CI/CD integration for data pipeline validation

Quick Start


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

# 2. Initialize DVC in your Git repository
dvc init

# 3. Configure remote storage
dvc remote add -d myremote s3://your-bucket/dvc-store

# 4. Start tracking data
dvc add data/training_data.csv
git add data/training_data.csv.dvc data/.gitignore
git commit -m "Track training data with DVC"

Prerequisites

  • Python 3.9+
  • Git
  • DVC 3.x
  • Remote storage (S3, GCS, Azure Blob, or SSH)

Contents


ml-data-versioning/
  config.example.yaml
  docs/
    overview.md
    patterns/
      pattern-01-*.md
    checklists/
      pre-deployment.md
  templates/
    config.yaml

Support

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

License

... continues with setup instructions, usage examples, and more.

📄 Code Sample .yaml preview

config.example.yaml # ML Data Versioning - Example Configuration # Copy this file to config.yaml and update values for your environment dvc: remote: name: "myremote" url: "s3://your-bucket/dvc-store" # For GCS: "gs://your-bucket/dvc-store" # For Azure: "azure://your-container/dvc-store" # For SSH: "ssh://user@host/path/to/storage" cache: local: ".dvc/cache" shared: false data: raw_data_dir: "data/raw" processed_data_dir: "data/processed" models_dir: "models" pipelines: preprocess: deps: - "data/raw/input.csv" - "src/preprocess.py" outs: - "data/processed/features.csv" cmd: "python src/preprocess.py" train: deps: - "data/processed/features.csv" - "src/train.py" outs: - "models/model.pkl" metrics: - "metrics/scores.json" cmd: "python src/train.py" logging: level: "INFO"
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