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Nir Barazida - Unlock the Full Potential of Jupyter Notebooks | PyData Global 2023
"Unlock the full potential of Jupyter Notebooks with Nir Barazida as he shares expert tips on organizing, versioning, and automating your data science workflows"
- Move code to modules: Break down single-notebook applications into separate modules to enable version control, reproducibility, and reuse.
- version Jupyter Notebooks: Version notebooks to enable tracking of changes and reproducibility.
- Use scripts for training and deployment: Use scripts for training and deployment to simplify the process and improve efficiency.
- Avoid ad-hoc versioning: Avoid ad-hoc versioning and instead use version control systems to manage code and data.
- Use CI/CD for automations: Use CI/CD for automations to streamline the process and ensure reproducibility.
- Break down modules: Break down large modules into smaller, more manageable pieces to improve maintenance and debugging.
- Use peer review: Use peer review to ensure code quality and reproducibility.
- Use task-oriented notebooks: Use task-oriented notebooks to focus on specific tasks and improve collaboration.
- Use libraries for data versioning: Use libraries for data versioning, such as nbdime, to enable tracking of changes and reproducibility.
- Use logging and tracking: Use logging and tracking to monitor and analyze the development process.
- Use collaboration tools: Use collaboration tools, such as Slack or GitHub, to facilitate communication and collaboration.
- Avoid global variables: Avoid using global variables to improve code organization and readability.
- Use nbdime: Use nbdime to enable cell and output diffing and to track changes in Jupyter Notebooks.
- Version data and models: Version data and models to enable tracking of changes and reproducibility.
- Use Daxup: Use Daxup to manage notebooks and enable collaboration.
- Avoid deploying notebooks to production: Avoid deploying notebooks to production and instead use scripts for training and deployment.