#development-practices

#development-practices

Every data science project must aim for production-level code delivery.

Many junior data scientists struggle with development skills, impacting their project execution.

Effective environment management is crucial in data science to avoid operational issues.

Using Python virtual environments in Docker replicates local development practices, enhancing code isolation and simplifying project management.

Code reuse accelerates development timelines.

Reuse enhances code quality, reduces bugs, and fosters collaboration.

The introduction of Software Bills of Materials (SBOMs) is reshaping expectations in software development, emphasizing the need for security awareness among developers.

Software development teams are adopting security automation to balance application security with speed and innovation.

Embracing security from the planning stage can enhance both security and developer efficiency.

Unchecked complexity in software architecture can hinder long-term progress despite initial success.

Recognizing and addressing subtle warning signs is crucial for maintaining a robust software architecture.