In traditional software engineering, code review is a well-established process. However, in the realm of machine learning (ML), reviewing code is not as straightforward. Machine learning workflows involve complex components such as data preprocessing, model training, experimentation, and evaluation—all of which must be reviewed with precision and context.
In this guide, we’ll walk through how to review machine learning code, what makes ML reviews different from software engineering reviews, and the best practices that every team should follow. Whether you’re a data scientist, ML engineer, or tech lead, mastering ML code review is essential to ensure model quality, performance, reproducibility, and long-term maintainability.
Why Machine Learning Code Reviews Matter
Unlike typical software projects, ML projects combine code with data science. A small mistake in preprocessing or an incorrect evaluation metric can invalidate weeks of work. Code reviews help:
- Prevent model bias and data leakage
- Catch reproducibility issues early
- Maintain model integrity and experiment traceability
- Encourage team learning and knowledge sharing
- Reduce technical debt in ML pipelines
Ultimately, reviewing ML code ensures trust in model outcomes.
Key Differences from Traditional Code Reviews
Before we dive into how to review machine learning code, it’s essential to understand how it differs from regular code reviews:
| Aspect | Software Engineering | Machine Learning |
|---|---|---|
| Focus | Logic, functionality, performance | Data flow, model correctness, metric selection |
| Output | Functional features or APIs | Statistical models with probabilistic outcomes |
| Determinism | Mostly deterministic | Often non-deterministic (due to random seeds) |
| Testing | Unit and integration tests | Requires validation on datasets and metrics |
| Complexity | Code-centric | Code + data + model interactions |
Prerequisites Before Reviewing ML Code
Before starting a review, the following should be in place:
- Code should be in a pull request (PR) with a clear description
- README or documentation describing the goal of the model or pipeline
- Access to training data or a synthetic sample if applicable
- Logs or summaries of training metrics and model evaluation
Make sure that you have the context of the problem being solved and the datasets being used.
How to Review Machine Learning Code: Step-by-Step
Effective machine learning code reviews require a deeper understanding than typical software reviews. Here’s a detailed, expanded walkthrough covering key areas to inspect, enriched with practical insights, questions to ask, and strategies to ensure quality, reproducibility, and maintainability.
1. Code Structure and Modularity
One of the first things to assess is the structure and modularity of the code. A well-structured codebase enhances readability, reusability, and ease of maintenance. Reviewers should ask:
- Is the code logically broken into modules for data ingestion, preprocessing, modeling, evaluation, and visualization?
- Are functions concise, adhering to the single-responsibility principle?
- Are class definitions clear and encapsulate related functionality?
Also, ensure the project follows common conventions like separating configuration files from logic (e.g., using YAML or JSON for parameters). Avoidance of hardcoding paths, magic numbers, or values improves flexibility and adaptability.
Tip: Look for reusable utility functions and check whether pipelines are designed to be extensible.
2. Data Pipeline and Preprocessing
Since machine learning models are only as good as the data they learn from, evaluating the data pipeline is critical. Start by checking if the dataset is loaded, cleaned, and preprocessed correctly:
- Is there any data leakage between training and test sets?
- Are data transformations (e.g., standardization, one-hot encoding) performed properly?
- Are preprocessing steps reproducible, and can they be applied identically to new inference data?
- Are missing values handled consistently?
A common error occurs when preprocessing is fit on the entire dataset instead of just the training set, causing leakage. The pipeline should use tools like Pipeline in scikit-learn or tf.data in TensorFlow to standardize steps and improve code clarity.
Red flag: If you see preprocessing steps inside a Jupyter notebook without modularization or reuse, flag it for refactoring.
3. Model Architecture and Training Logic
This section encompasses model instantiation, training, and tuning. Here are key questions:
- Is the model choice suitable for the task (classification, regression, time-series)?
- Are model parameters customizable via config files or CLI args?
- Are frameworks like PyTorch, TensorFlow, or scikit-learn used appropriately?
- Are device (CPU/GPU) management and memory optimizations handled explicitly?
- Are training loops robust with checkpointing, validation, and early stopping logic?
When reviewing deep learning code, assess whether the model architecture is clearly defined and modularized. Look for mistakes in loss function setup, incorrect tensor shapes, or missing dropout layers in overfitting scenarios.
4. Metrics and Evaluation Logic
Good ML evaluation goes far beyond printing accuracy scores. Confirm that:
- Appropriate metrics are chosen based on problem type (e.g., AUC, F1 for classification; RMSE for regression)
- Cross-validation or stratified sampling is implemented to generalize model performance
- Confusion matrices, ROC curves, or other visual tools are provided
- Evaluation avoids contamination from training data
- Error analysis is performed to understand misclassifications or residuals
Models should not only be performant but interpretable. Encourage the use of tools like SHAP or LIME to help debug complex model behavior.
5. Experiment Tracking and Logging
Tracking experiments is crucial in ML due to its non-deterministic nature. A reviewer should verify:
- Use of experiment tracking tools like MLflow, Neptune, or W&B
- Storage of key hyperparameters, training duration, and final metrics
- Association of model artifacts (e.g.,
.pkl,.pt) with experiment runs - Version control for each experiment, especially if models evolve quickly
If the code lacks logging and tracking, it’s nearly impossible to reproduce results or roll back to previous versions. Even basic logging with logging module or print statements can be a start.
6. Reproducibility and Environment Setup
Being able to reproduce results is critical. A reviewer should ask:
- Is there a
requirements.txt,environment.yml, orpyproject.toml? - Are Python seeds (
random,numpy,torch, etc.) set for deterministic behavior? - Is the model training run consistent across machines or reruns?
- Are model weights, checkpoints, or outputs saved systematically?
Encourage the use of tools like DVC to version datasets and models or Docker to containerize environments. Without reproducibility, results are non-trustworthy.
7. Code Quality, Style, and Documentation
Even the most innovative models can become liabilities if the code is unreadable. Focus on:
- PEP8 compliance, naming conventions, and consistent formatting
- Adequate docstrings for functions, classes, and modules
- Explanatory comments where logic is non-obvious
- Clean, well-structured Jupyter notebooks with limited output and runtime artifacts removed
- Use of linters (e.g., flake8, pylint) and formatters (e.g., black)
Bonus: Review the Git history for meaningful commit messages and organized code evolution.
8. Testing and CI Integration
Testing ML code is often overlooked, yet essential. As a reviewer:
- Check for unit tests on key functions (data processing, metrics, model evaluation)
- Look for integration tests using mock or synthetic data
- Verify that CI tools like GitHub Actions run tests on every pull request
- Confirm expected behavior in corner cases (e.g., missing data, zero variance columns)
Testing model pipelines (e.g., sklearn Pipeline, Keras Model) with simple assertions can prevent major production bugs.
Suggestion: Include test scripts or notebooks to validate models on held-out or adversarial data.
By following this in-depth approach to reviewing machine learning code, you’ll ensure higher quality, more reliable models that are easier to maintain and scale. This ultimately leads to better outcomes in both experimentation and production.
Tools That Aid ML Code Review
Several tools can streamline the review process:
| Tool | Purpose |
| Black / isort | Code formatting |
| Pylint / Flake8 | Static code analysis |
| PyTest | Unit testing |
| MLflow / W&B | Experiment tracking |
| DVC | Data and model version control |
| GitHub Actions / GitLab CI | Run tests in CI pipelines |
These tools help ensure that code quality and performance standards are consistently enforced.
Common Pitfalls to Watch Out For
- Data leakage between training and test sets
- Random seed not fixed, leading to non-reproducible results
- Training and inference preprocessing mismatch
- Incomplete evaluation (e.g., using accuracy for imbalanced datasets)
- Lack of documentation or explanation for chosen models
- Overly complex model for simple problems (overengineering)
Tips for Effective Peer Reviews
- Be constructive: Phrase feedback in a helpful tone
- Ask clarifying questions: Don’t assume intent—ask why something was done a certain way
- Use checklists: Build an internal checklist for your team’s review process
- Share knowledge: Reviews are a great opportunity for upskilling and cross-learning
- Balance thoroughness and speed: Avoid overengineering during the review itself
Conclusion
Reviewing ML code isn’t just about checking Python syntax. It’s about validating that the model is reliable, ethical, and production-ready. By following a structured approach—checking for modular design, correct data handling, meaningful evaluation, and reproducibility—you can ensure your team’s machine learning models are not only performant but also trustworthy.
Knowing how to review machine learning code effectively will improve the quality of your projects and foster better collaboration within your team. It’s a habit worth building.