Transitioning to Implementing Reliable and Efficient Code in Data Science

Catherine Nelson is a freelance data scientist and writer. She is currently working on the forthcoming O’Reilly book "Software Engineering for Data Scientists”. Why All Data Scientists Should Learn Software Engineering Principles // MLOps podcast #245 with Catherine Nelson, a freelance Data Scientist. A big thank you to LatticeFlow AI for sponsoring this episode! LatticeFlow AI - https://latticeflow.ai/ // Abstract Data scientists have a reputation for writing bad code. This quote from Reddit sums up how many people feel: “It's honestly unbelievable and frustrating how many Data Scientists suck at writing good code.” But as data science projects grow, and because the job now often includes deploying ML models, it's increasingly important for DSs to learn fundamental SWE principles such as keeping your code modular, making sure your code is readable by other people and so on. The exploratory nature of DS projects means that you can't be sure where you will end up at the start of a project, but there's still a lot you can do to standardize the code you write. // Bio Catherine Nelson is the author of "Software Engineering for Data Scientists", a guide for data scientists who want to level up their coding skills, published by O'Reilly in May 2024. She is currently consulting for GenAI startups and providing mentorship and career coaching to data scientists. Previously, she was a Principal Data Scientist at SAP Concur. She has extensive experience deploying NLP models to production and evaluating ML systems, and she is also co-author of the book "Building Machine Learning Pipelines", published by O'Reilly in 2020. In her previous career as a geophysicist, she studied ancient volcanoes and explored for oil in Greenland. Catherine has a PhD in geophysics from Durham University and a Masters of Earth Sciences from Oxford University. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Software Engineering for Data Scientists book by Catherine Nelson: https://learning.oreilly.com/library/view/software-engineering-for/9781098136192/ https://www.amazon.com/Software-Engineering-Data-Scientists-Notebooks/dp/1098136209 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Catherine on LinkedIn: https://www.linkedin.com/in/catherinenelson1/ Timestamps: [00:00] Catherine's preferred coffee [00:15] Takeaways [02:38] Meeting magic: Embracing serenity [06:23] The Software Engineering for Data Scientists book [10:41] Exploring ideas rapidly [12:52] Bridging Data Science gaps [16:17] Data poisoning concerns [18:26] Transitioning from a data scientist to a machine learning engineer [21:53] Rapid Prototyping vs Thorough Development [23:45] Data scientists take ownership [25:53] Data scientists' role balance [30:30] Understanding system design process [36:00] Data scientists and Kubernetes [41:33 - 43:03] LatticeFlow AI Ad [43:05] The Future of Data Science [45:09] Data scientists analyzing models [46:46] Tools gaps in prompt tracking [50:44] Learnings from writing the book