Data versioning in machine learning is the process of tracking changes to datasets and models over time, helping ensure reproducibility, enabling collaboration, and managing model lifecycles. Versioning tools help manage data, model artifacts, configurations, and even code changes. Here’s a comprehensive guide on popular data versioning tools, how they work, and examples.
Data versioning refers to tracking and managing versions of datasets and models used in ML experiments. It allows you to:
- Reproduce results by tracking specific dataset versions.
- Compare model performances with different data versions.
- Maintain a history of changes in the data and model artifacts.
- Simplify collaboration and deployment by organizing and sharing models.
Here are some widely-used data versioning tools in machine learning:
- DVC (Data Version Control)
- MLflow
- Weights & Biases
- Pachyderm
- Git-LFS (Large File Storage)
- Delta Lake (Databricks)
DVC (Data Version Control)
- DVC (Data Version Control)
- DVC is an open-source tool designed for versioning datasets, models, and machine learning pipelines.
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- Git-like interface for data and model versioning.
- Works seamlessly with Git, enabling data to be versioned in parallel with code.
- Efficient storage using links and cloud storage (like AWS, GCP, and Azure).
- Support for creating pipelines to manage ML workflows.
Basic Usage Example:
- bash
- Initialize a DVC repository
- dvc init
- Track data file
- dvc add data/raw_data.csv
- Commit to Git
- git add data/raw_data.csv.dvc .gitignore
- git commit -m "Add raw data with DVC"
- Push data to remote storage
- dvc remote add -d myremote s3://bucket/path
- dvc push
- Works well for managing large datasets and model files.
- Integrates smoothly with Git workflows.
- May be challenging to configure remote storage.
- Requires some knowledge of Git to use effectively.
MLflow is an open-source platform for managing the ML lifecycle, including tracking experiments, packaging code, and sharing models.
Key Features:
- Experiment tracking for logging model metrics, parameters, and artifacts.
- Model registry to version models and manage deployment stages.
- Works with cloud storage and integrates with tools like TensorFlow, PyTorch, and scikit-learn.
Basic Usage Example:
python
import mlflow
Start a new experiment
mlflow.start_run()
Log parameters, metrics, and model
mlflow.log_param("learning_rate", 0.01)
mlflow.log_metric("accuracy", 0.95)
mlflow.sklearn.log_model(model, "model")
End the run
mlflow.end_run()
Benefits:
Start a new experiment
mlflow.start_run()
Log parameters, metrics, and model
mlflow.log_param("learning_rate", 0.01)
mlflow.log_metric("accuracy", 0.95)
mlflow.sklearn.log_model(model, "model")
End the run
mlflow.end_run()
- Easy setup for tracking experiments, managing models, and serving.
- Rich UI for visualizing metrics and comparing model performance.
- Requires additional setup to use a remote server or cloud storage.
Weights & Biases is a popular tool for experiment tracking, versioning datasets and models, and collaborating on ML projects.
Key Features:
- Real-time experiment tracking with metrics, parameters, and artifacts.
- Dataset and model versioning with visualizations.
- Integrations with major ML frameworks (TensorFlow, PyTorch, Keras, etc.).
- Collaboration features for teams working on ML projects.
Basic Usage Example:
python
import wandb
Initialize a new run
wandb.init(project="my_project")
Log parameters and metrics
wandb.config.learning_rate = 0.01
wandb.log({"accuracy": 0.95, "loss": 0.05})
Save model as an artifact
wandb.save("model.h5")
Benefits:
- User-friendly UI and real-time updates on metrics.
- Powerful collaboration features.
- Cloud-based, so it requires an internet connection.
- Limited free tier; premium features require a subscription.
Pachyderm combines data versioning and pipeline management in one platform. It is designed to work with large-scale datasets and complex ML workflows.
Key Features:
- Data versioning using containers, which helps with large datasets.
- Enables data lineage tracking, ensuring that all changes to the data are recorded.
- Can handle complex pipelines with automatic dependency handling.
Basic Usage Example:
- Pachyderm’s workflow setup is based on Docker and Kubernetes, so it requires familiarity with containers. A typical use case involves creating a pipeline using a Docker image to process data, which is versioned and stored in a Pachyderm repository.
- Ideal for managing complex data science workflows.
- Integrates with cloud storage and Kubernetes.
- Complex setup and requires knowledge of Docker and Kubernetes.
- Not ideal for small projects or quick experimentation.
Git-LFS is an extension to Git that allows you to store large files (e.g., datasets, model weights) in a more efficient way without bloating your Git repository.
Key Features:
- Versioning for large files without affecting Git performance.
- Works with Git commands, so it’s easy to learn for Git users.
Basic Usage Example:
bash
Install Git-LFS
git lfs install
Track a large file
git lfs track "large_dataset.csv"
Add to Git as usual
git add large_dataset.csv
git commit -m "Add large dataset with LFS"
git push
Benefits:
Allows versioning of large files with minimal overhead.
Limitations:
- Not specifically designed for ML, lacks experiment tracking features.
- Requires additional storage if used on cloud-hosted Git repositories.
Let’s consider an example where an ML team works on a predictive maintenance model for a manufacturing company. The dataset contains historical machine sensor data, and they want to experiment with different feature engineering techniques and algorithms.
Step 1: Version the dataset using DVC. This allows the team to work with specific versions of the data and track changes (e.g., removing outliers, filling missing values).
Step 2: Use MLflow to track various model configurations, hyperparameters, and performance metrics (e.g., accuracy, recall).
Step 3: The best-performing model is saved as an artifact in MLflow or W&B for future use and deployment.
Step 4: If changes are made to the dataset, DVC automatically detects them, and the team can re-run the model training pipeline to update the results.
5. Choosing the Right Data Versioning Tool
Consider the following factors:
- Project Size: DVC and Git-LFS are excellent for projects that don’t need complex pipelines. Pachyderm suits large, complex workflows.
- Experiment Tracking: MLflow and W&B provide robust tracking for experiments, while DVC is more focused on data versioning.
- Collaboration Needs: Tools like W&B excel in team collaboration with real-time metrics sharing.
- Infrastructure Setup: If you’re comfortable with Kubernetes, Pachyderm can handle complex, large-scale workflows.