Talk Python To Me
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Your snips
[04:07] From Geologist to Data Scientist
[21:20] Adopt Software Engineering Mindsets
[24:06] Break Down Notebook Code
[30:31] Refactor with Conversion Tools
[36:08] LLMs Help but Need Expertise
[40:58] Exploration vs Production Code
[43:34] Standardize and Automate ML Production
[46:10] Learn DevOps Incrementally
[50:49] Share and Refactor Code
I just finished listening to this podcast:
𝗣𝗼𝗱𝗰𝗮𝘀𝘁 Talk Python To Me with Michael Kennedy & Catherine Nelson - 𝗘𝗽𝗶𝘀𝗼𝗱𝗲: #511: From Notebooks to Production Data Science Systems
𝗗𝗮𝘁𝗲: June 28, 2025
𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆
Moving from exploratory notebooks to production-ready code isn't just a technical transition—it's a signal your data science project has succeeded and is ready to deliver real value.
𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀:
In finance, the gap between analysis and implementation is where many ML initiatives die. Standardizing your production workflow with frameworks like TensorFlow Extended enables consistent model deployment, automated retraining, and proper monitoring—essential for regulatory compliance and reliable financial predictions.
𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 🧠
I've seen too many teams get stuck in perpetual "notebook mode" because the production transition feels daunting. This episode reminded me that this skill gap isn't insurmountable—it's about taking incremental steps toward DevOps practices while maintaining a healthy skepticism about what your models are actually doing in production.
Follow up
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#TalkPythonToMe #DataScience #MLOps