Explore clean code practices for data scientists in this insightful MLOps Coffee Sessions podcast episode featuring Matt Sharp, Data Developer at Shopify. Gain valuable insights into Shopify's real-time serving platform, Merlin, which powers recommender systems, inbox classification, and fraud detection. Discover Matt's journey from chemical engineering to data science and data engineering, and learn about his upcoming book on LLMs in production. Delve into topics such as the definition of clean code, transitioning from Jupyter notebooks, exploratory data analysis, and best practices for production-level software engineering. Understand the importance of communicable clean code and how it can enhance your career as a data scientist. Get a sneak peek into upcoming Shopify projects and learn about the early access opportunity for Matt's LLMs in Production book.
Clean Code for Data Scientists - MLOps Coffee Sessions
Overview
Syllabus
[] Matt's preferred drink
[] Takeaways
[] Watch out for Matt's LLMs in Production book coming up!
[] Please like, share, subscribe, and join the upcoming LLMs in Production Conference Part 2!
[] Raising awareness about the fundamental problems of writing clean code
[] Definition of clean code
[] Communicable clean code
[] Getting out of Jupyter notebooks at the end of their life
[] Exploratory data analysis
[] Most popular post on LinkedIn
[] Zilliz Ad
[] Best practices on production-level software engineering
[] Merlin
[] Upcoming Shopify projects
[] Matt's upcoming LLMs in Production book
[] LLMs in Production book Early Access
[] Wrap up
Taught by
MLOps.community
