Explore the importance of software engineering principles for data scientists in this 53-minute podcast episode featuring Catherine Nelson, a freelance Data Scientist and author. Delve into topics such as writing modular and readable code, standardizing coding practices in exploratory projects, and bridging the gap between data science and software engineering. Learn about Catherine's experience deploying NLP models to production, evaluating ML systems, and her insights from writing "Software Engineering for Data Scientists." Discover the challenges of transitioning from data scientist to machine learning engineer, the balance of rapid prototyping and thorough development, and the future of data science. Gain valuable insights on data poisoning concerns, system design processes, and the role of data scientists in analyzing models and tracking prompts in AI development.
Why Data Scientists Should Learn Software Engineering Principles
Overview
Syllabus
[] Catherine's preferred coffee
[] Takeaways
[] Meeting magic: Embracing serenity
[] The Software Engineering for Data Scientists book
[] Exploring ideas rapidly
[] Bridging Data Science gaps
[] Data poisoning concerns
[] Transitioning from a data scientist to a machine learning engineer
[] Rapid Prototyping vs Thorough Development
[] Data scientists take ownership
[] Data scientists' role balance
[] Understanding system design process
[] Data scientists and Kubernetes
[41:33 - ] LatticeFlow AI Ad
[] The Future of Data Science
[] Data scientists analyzing models
[] Tools gaps in prompt tracking
[] Learnings from writing the book
Taught by
MLOps.community
