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decodingml / llm-twin-course

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๐Ÿค– ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป for ๐—ณ๐—ฟ๐—ฒ๐—ฒ how to ๐—ฏ๐˜‚๐—ถ๐—น๐—ฑ an end-to-end ๐—ฝ๐—ฟ๐—ผ๐—ฑ๐˜‚๐—ฐ๐˜๐—ถ๐—ผ๐—ป-๐—ฟ๐—ฒ๐—ฎ๐—ฑ๐˜† ๐—Ÿ๐—Ÿ๐—  & ๐—ฅ๐—”๐—š ๐˜€๐˜†๐˜€๐˜๐—ฒ๐—บ using ๐—Ÿ๐—Ÿ๐— ๐—ข๐—ฝ๐˜€ best practices: ~ ๐˜ด๐˜ฐ๐˜ถ๐˜ณ๐˜ค๐˜ฆ ๐˜ค๐˜ฐ๐˜ฅ๐˜ฆ + 11 ๐˜ฉ๐˜ข๐˜ฏ๐˜ฅ๐˜ด-๐˜ฐ๐˜ฏ ๐˜ญ๐˜ฆ๐˜ด๐˜ด๐˜ฐ๐˜ฏ๐˜ด

docker aws course infrastructure-as-code machine-learning-engineering comet-ml rag mlops pulumi qdrant large-language-models bytewax generative-ai qwak llmops ml-system-design superlinked
  • Updated May 22, 2024
  • Python

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