René's URL Explorer Experiment

{"@context":"https://schema.org","@type":"DiscussionForumPosting","headline":"MLOps Code Repository Review","articleBody":"This repository demonstrates a well-structured MLOps project, exhibiting characteristics across multiple maturity levels. It leverages modern Python tooling and MLOps practices, making it a solid foundation for building and deploying machine learning applications.\n\n**General Summary:**\n\nThe repository showcases a mature MLOps project, evident from its comprehensive tooling, CI/CD workflows, documentation, and adherence to software engineering principles. The use of `cruft` for project templating, `uv` for package management, and `MLflow` for experiment tracking and model registry highlights a commitment to reproducibility, automation, and collaboration. The inclusion of notebooks for data processing and model explanation further enhances the project's usability and educational value.\n\n**Guidelines for Improvements:**\n\nWhile the repository demonstrates a high level of MLOps maturity, there are areas where further improvements can be made to reach GA (General Availability) level:\n\n*   **Enforced Test Coverage:**\n    *   **Issue:** The CI workflow does not explicitly enforce a minimum test coverage percentage.\n    *   **Fix:** Modify the `check-coverage` task in `tasks/check.just` and the `check.yml` workflow to include a check that fails the build if the coverage falls below a defined threshold (e.g., 80%). This ensures that all new code is adequately tested.\n\n*   **Deterministic Builds:**\n    *   **Issue:** While `uv` and constraints are used, a lock file (`uv.lock`) is not present to guarantee deterministic builds.\n    *   **Fix:** Generate and commit a `uv.lock` file to the repository. Update the build process (e.g., in `justfile` or CI workflow) to use the lock file during package installation, ensuring that the exact same versions of dependencies are used across all environments.\n\n*   **Formal Release Management:**\n    *   **Issue:** While a `CHANGELOG.md` exists and Git tags are likely used, the CI/CD workflow doesn't fully automate the release process, including generating release notes.\n    *   **Fix:** Enhance the `publish.yml` workflow to automatically create GitHub releases with release notes based on the `CHANGELOG.md` content when a new tag is pushed. This can be achieved using tools like `semantic-release` or custom scripts that parse the changelog and generate the release notes.\n\n*   **Comprehensive Documentation:**\n    *   **Issue:** While API documentation is generated, the README lacks badges for key metrics like test coverage and code quality.\n    *   **Fix:** Add badges to the `README.md` file to display the build status, test coverage percentage, code quality score (e.g., from Ruff), and other relevant metrics. This provides a quick overview of the project's health and maturity.\n\n*   **Monitoring/Evaluation Artifacts:**\n    *   **Issue:** The code does not include explicit jobs or scripts for model evaluation using tools like `mlflow.evaluate` or `Evidently` to generate evaluation reports.\n    *   **Fix:** Implement model evaluation jobs or scripts that use tools like `mlflow.evaluate` or `Evidently` to compute relevant metrics and generate evaluation reports. These reports should be saved as artifacts in MLflow for tracking and analysis.\n\n*   **Lineage Tracking:**\n    *   **Issue:** The code does not demonstrate the use of lineage tracking features like `mlflow.log_input` with MLflow Datasets.\n    *   **Fix:** Incorporate lineage tracking features into the code, particularly in data processing and model training jobs. Use `mlflow.log_input` with MLflow Datasets to track the data sources and transformations used in each step of the pipeline.\n\n*   **Explainability Artifacts:**\n    *   **Issue:** The code does not include jobs or scripts to generate model explanations (e.g., using SHAP) and save these as artifacts.\n    *   **Fix:** Add jobs or scripts to generate model explanations using tools like SHAP and save these explanations as artifacts in MLflow. This allows for better understanding and debugging of model behavior.\n\n*   **Infrastructure Metrics Logging:**\n    *   **Issue:** The code does not utilize system metrics logging (e.g., `mlflow.start_run(log_system_metrics=True)`).\n    *   **Fix:** Enable system metrics logging in relevant code sections (e.g., model training jobs) by using `mlflow.start_run(log_system_metrics=True)`. This provides insights into the infrastructure resources used during model training and evaluation.\n","author":{"url":"https://github.com/fmind","@type":"Person","name":"fmind"},"datePublished":"2025-04-12T13:05:53.000Z","interactionStatistic":{"@type":"InteractionCounter","interactionType":"https://schema.org/CommentAction","userInteractionCount":0},"url":"https://github.com/72/mlops-python-package/issues/72"}