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[Part 5]
💭 If you're reading this & you've been involved with developing an ML Platform, how did you approach the "centralize vs distributed" discussion? What worked? What failed?
👇 Let me know in the comments below!
#mlops #mlplatform #ai #strategy #dataops #dataengineering #devops #platformengineering #platformdesign #mlengineer
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🧠 Everyone else: <LLM Experts, producing multi-modal Gen AI systems. >
🤓 Me: <Still troubleshooting that lambda function to calculate Euclidean distance of lat/long columns in Polars Dataframe for a sample project in Colab. > 😅
#datascience #MLops #productionml #AI #mlengineer
Build vs Buy, Centralized vs Distrib...
These are false dichotomies as many orgs follow a similar path of:
1️⃣ When building new, first centralize
2️⃣ Move to distrib. model to avoid blocking
🔁 Start cycle again
So really, pull & release, pull & release is closer to the actual cadence & is also why the responses are so....lame when panels are asked "How do you navigate the Build vs Buy dilemma?" for the 100th time.
#MLops #mlengineer #datascience
Simple Numpy quiz
What is the answer?
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I think it's tremendously impt to understand lower-level concepts to become a great #datascientist and #mlengineer for the long-haul.
But if you're struggling to break in, first evaluate whether the gap is actually understanding high-lvl concepts. #🐘
But if you're struggling to break into #datascience, #mlengineer , #mlops , first evaluate whether the gap is truly at the programming level and not at the workflow, project, and practices level.
#🐘
8/8
#datascience #mlengineer #MLops