General best practices for data product development
To ensure successful outcomes and maintain high standards, top data science, artificial intelligence, and machine learning teams employ the following cross-cutting best practices throughout the development process:
- Version control and reproducibility:
- Implement robust version control systems for code, data, and models. For code version control, Git and Git-based software such as GitHub, GitLab, and Bitbucket are common tools. For data and model version control, software such as Data Version Control (DVC) and MLflow are also common approaches to tracking data, model artifacts, and model training experiments.
- Ensure reproducibility by documenting dependencies, configurations, and experimental setups.
- Use containerization technologies to create reproducible environments for development and deployment.
- Clear documentation and knowledge management:
- Maintain clear and comprehensive documentation for data, code, and models
- Establish...
