The Notebook Anti-Pattern - KDnuggets

Abstraction: Why Jupyter notebooks are unsuitable for ML production pipelines

Key points:

• Notebooks are appropriate for data analysis, experimentation, teaching, and one-time tasks only
• Key failure modes in production: no CI support, untestable code, unreadable version control diffs, poor collaboration (merge conflicts on state), global state causing reproducibility failures
• "One click deployment" notebook tools encourage the anti-pattern by dressing exploration tools as production infrastructure
• No package management per notebook; relies on ambient environment
• Solution: extract code to scripts/modules with proper software engineering practices (tests, versioning, CI) before production
• Two team models addressed: full-stack data scientists and split DS/ML-engineer teams; both should avoid notebook-to-production pipelines

Connections: Jupyter · ML Engineering · Mlops · Software Engineering

Source: https://www.kdnuggets.com/2019/11/notebook-anti-pattern.html