How to document and version preprocessing logic in ML training

Documenting and versioning preprocessing logic in machine learning (ML) training is essential for maintaining reproducibility, ensuring consistency, and enabling team collaboration. Here’s a step-by-step approach on how to document and version your preprocessing logic effectively:

1. Define Preprocessing Requirements

• Data Sources: Document the data sources (databases, APIs, datasets) and formats (CSV, JSON, Parquet) involved in preprocessing.

• Preprocessing Goals: Specify what preprocessing is being done and why (e.g., normalization, feature extraction, handling missing values, encoding).

• Input/Output Formats: Clearly define the structure of the input and output data, including data types, column names, and shapes.

2. Modularize the Preprocessing Logic

• Separate Logic into Functions/Modules: Break down your preprocessing pipeline into smaller, reusable components (e.g., handling missing values, scaling features, encoding categorical variables).

• Create Functions for Repeated Steps: Ensure that each function performs one distinct task and is reusable across different experiments or models.

• Example of Functions:

  • handle_missing_data(data): Deals with missing data.

  • normalize_features(data): Normalizes numerical features.

  • encode_categorical(data): Encodes categorical variables.

3. Version Control of Preprocessing Code

• Git or Other Version Control Systems (VCS): Store the preprocessing scripts in a Git repository or similar version control system. Make sure every change to the preprocessing logic is versioned with detailed commit messages.

• Branching: Use feature branches or tags for major changes (e.g., switching from one encoding strategy to another) and document these changes in a changelog.

• Code Review Process: Implement peer review for changes in preprocessing code, especially when they affect the entire pipeline or model.

4. Track Preprocessing Configurations

• Configuration Files: Store hyperparameters or preprocessing settings (e.g., scaling factors, encoding techniques) in external configuration files such as JSON, YAML, or TOML.

  • Example: config.json

    json
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    {
      "scaling_method": "StandardScaler",
      "missing_data_strategy": "mean_imputation",
      "categorical_encoding": "one_hot"
    }
    

• Link to Codebase: Ensure that each version of your codebase points to a specific configuration file, either by storing the version number in the configuration file or referencing it in your model versioning system.

5. Document Preprocessing Steps

• README Files: Create a detailed README or documentation for your preprocessing logic. This should include:

  • Overview of the pipeline

  • Steps involved in the preprocessing process

  • Expected inputs and outputs

  • External dependencies (e.g., libraries, tools)

  • Example usage (code snippets for common use cases)

• Docstrings: Add docstrings to your functions and classes in the preprocessing scripts. These should include a description of what the function does, its parameters, and its return value.

  • Example:

    python
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    def handle_missing_data(data, strategy="mean"):
        """
        Handles missing data in the dataset using the specified strategy.
        
        Parameters:
        - data: pandas.DataFrame, the dataset with missing values.
        - strategy: str, strategy for imputing missing values ("mean", "median", etc.).
        
        Returns:
        - pandas.DataFrame, the dataset with imputed values.
        """
    

6. Versioning Preprocessing Outputs

• Data Versioning: Use tools like DVC (Data Version Control) or Pachyderm to track the versions of datasets used in your training pipeline. This ensures that your training data, including preprocessing steps, can be traced back to the exact state when the model was trained.

• Store Preprocessed Data: If your preprocessing steps are time-consuming, consider storing the preprocessed data in a separate location (e.g., cloud storage, data warehouse) with a version tag that corresponds to the preprocessing version.

7. Automate Preprocessing Pipelines

• Pipeline Orchestration Tools: Use tools like Apache Airflow, Kubeflow, or Prefect to automate and track your preprocessing pipelines. These tools help in scheduling, monitoring, and logging each step in the preprocessing pipeline.

• Log Key Information: Implement logging for preprocessing steps, especially for long-running tasks. This can include the time taken, data sample statistics (e.g., mean, variance), and any issues encountered.

8. Experiment Tracking

• Track Experiment Parameters: If you’re using machine learning platforms like MLflow, TensorBoard, or Weights & Biases, store your preprocessing configuration and parameters as part of your experiment metadata. This ensures that you can always trace back the preprocessing settings used in each model experiment.

• Versioning with GitHub Actions or CI/CD: If you are using CI/CD pipelines to trigger model training, ensure that the preprocessing code and configuration are part of the pipeline and versioned accordingly.

9. Documentation in Notebooks or Jupyter

• Interactive Documentation: Jupyter Notebooks can be used to document and test preprocessing steps interactively. You can create notebooks that demonstrate how the preprocessing steps work, along with explanations and visualizations.

• Link Notebooks to Git Repositories: Version your Jupyter notebooks and associate them with specific commits in your Git repositories to track changes over time.

10. Change Management for Preprocessing Logic

• Track Changes: Use change management strategies like semantic versioning (e.g., v1.0, v1.1, v2.0) for preprocessing logic to signify significant updates or backward-compatible changes.

• Deprecation of Old Logic: If you’re removing or changing key preprocessing steps, mark older versions as deprecated and document how to migrate to the new version.

By combining version control, detailed documentation, and automation tools, you ensure that your preprocessing logic is both reproducible and easily traceable.