Databricks & Local LLMs: Data Engineering to Deployment โ€” LearnFlat

Databricks & Local LLMs: Data Engineering to Deployment

Learn to leverage Databricks for essential data engineering and analytics tasks, then deploy and manage local Large Language Models for robust data science workflows.

โ˜… 3.9 (12) โฑ 1 oras 52 min ๐Ÿ“š 3 aralin ๐ŸŽง Audio version

Tungkol sa kursong ito

Unleash the power of your data with Databricks and learn to deploy Large Language Models directly on your local infrastructure. This course provides a foundational understanding of using Databricks for data preparation and analysis, then guides you through the process of setting up and running local Large Language Models (LLMs) for versatile data science applications. What you'll learn: * Learn to navigate the Databricks environment and utilize its core functionalities * Understand the principles of data engineering and analytics within Databricks * Deploy local Large Language Models like Mixtral using frameworks such as Hugging Face Candle * Implement local LLMs effectively with tools including Mozilla llamafile * Practice foundational prompt engineering techniques for interacting with local LLMs * Apply best practices for managing and evaluating locally deployed models The course begins with an introduction to Databricks fundamentals, covering data ingestion, transformation, and analysis. It then transitions into the practical aspects of selecting, deploying, and interacting with local Large Language Models, culminating in hands-on application scenarios. This course is designed for beginners in data science, data engineering, or anyone looking to combine robust data platforms with on-premise AI capabilities. No prior experience with Databricks or LLMs is required, just a basic understanding of programming concepts. Start building your expertise in modern data and AI workflows today.

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  • ๐ŸŽง Kasama ang audio version
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  • โ™พ๏ธ Lifetime access
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  • ๐Ÿ“ฑ Telepono o computer
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  • ๐Ÿ’ธ 14-day refund
    Walang tanong
  • โšก Maikli at focused
    1 oras 52 min ng practical content

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