Doing Data Science on Cloud

on YouTube ↗35m

" summary

Compares cloud infrastructure options from AWS, Google Cloud and Microsoft for running data science workloads (e.g., multi‑GPU training, Spark transformations, NumPy jobs) with attention to cost, auto‑scaling and versioning. Covers vendor‑specific and vendor‑independent deployment strategies, use of MLaaS offerings, installing non‑bundled libraries and leveraging custom hardware resources.

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