𝐌𝐨𝐬𝐭 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭𝐬 𝐤𝐧𝐨𝐰 𝐏𝐲𝐭𝐡𝐨𝐧. 𝐅𝐚𝐫 𝐟𝐞𝐰𝐞𝐫 𝐤𝐧𝐨𝐰 𝐰𝐡𝐚𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐢𝐬 𝐝𝐨𝐢𝐧𝐠. Most of us learned Python as a tool for data manipulation and model training — not as a language with a runtime, a memory model, and a concurrency system that behave in very specific ways. There's a difference — and it shows up the moment you move from a notebook to production. --- I wrote a 4-part series on Python internals that helps developers avoid the most common pitfalls I've seen in 7+ years of bringing Python projects into production. 𝐏𝐚𝐫𝐭 1 - 📌 𝐏𝐲𝐭𝐡𝐨𝐧 𝐔𝐧𝐝𝐞𝐫 𝐭𝐡𝐞 𝐇𝐨𝐨𝐝: 𝐓𝐡𝐞 𝐆𝐈𝐋, 𝐁𝐲𝐭𝐞𝐜𝐨𝐝𝐞 & 𝐌𝐞𝐦𝐨𝐫𝐲 𝐌𝐨𝐝𝐞𝐥 𝐄𝐯𝐞𝐫𝐲 𝐌𝐋 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐒𝐡𝐨𝐮𝐥𝐝 𝐊𝐧𝐨𝐰. 🔗 Link in the comments. Coming up in the series: → #2: Concurrency & Parallelism (cores, processes, asyncio) → #3: High-Throughput ML APIs with FastAPI → #4: Memory Management & Lazy Evaluation #Python #MachineLearning #MLEngineering #DataScience #SoftwareEngineering Full article: https://lnkd.in/dGsb2Sm3
An excellent reminder for data scientists transitioning from notebooks to production environments. Understanding Python’s internals, like the GIL, memory model, and concurrency, is crucial to avoid common pitfalls. This series is an invaluable resource for those looking to optimize their Python code for real-world applications. Looking forward to the next parts!
This is fair, but don't forget the "scientist" part of Data Scientist. Not all Data Scientists necessarily have to create product code, nor use Python as a tool, to begin with (they can use Matlab or R). Great deal of the outcomes of NASA's Data Science team working on the Kepler Mission was achieved in Matlab (e.g., see https://github.com/nasa/kepler-pipeline/tree/master/source-code/matlab), and by "outcome" I mean the most profound possible: the discovery of exoplanets elsewhere in the universe. To pick another example, data science teams at bio-pharmaceutics companies today also use R for their statistical analyses. Not everything is production code, not everything has to be deployed, specially in data science. There are fore sure data scientists who need to build production codes, but they are not the total ensemble of DS out there.