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Databricks: Large Language Models: Application through Production

4.6 stars
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This course is aimed at developers, data scientists, and engineers looking to build LLM-centric applications with the latest and most popular frameworks. By the end of this course, you will have built an end-to-end LLM workflow that is ready for production!

Large Language Models: Application through Production
6 weeks
4–10 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

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Starts Apr 26
Ends May 31
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Large Language Models: Application through Production

About this course

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This course is aimed at developers, data scientists, and engineers looking to build LLM-centric applications with the latest and most popular frameworks. You will use Hugging Face to solve natural language processing (NLP) problems, leverage LangChain to perform complex, multi-stage tasks, and deep-dive into prompt engineering. You will use data embeddings and vector databases to augment LLM pipelines. Additionally, you will fine-tune LLMs with domain-specific data to improve performance and cost, as well as identify the benefits and drawbacks of proprietary models. You will assess societal, safety, and ethical considerations of using LLMs. Finally, you will learn how to deploy your models at scale, leveraging LLMOps best practices.

By the end of this course, you will have built an end-to-end LLM workflow that is ready for production!

At a glance

  • Institution: Databricks
  • Subject: Computer Science
  • Level: Intermediate
  • Prerequisites:

    • Intermediate-level experience with Python

    • Working knowledge of machine learning and deep learning is helpful

  • Language: English
  • Video Transcript: English
  • Associated programs:
  • Associated skills:Natural Language Processing, Workflow Management

What you'll learn

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  • How to apply Generative AI (GenAI) / LLMs to real-world problems in natural language processing (NLP) using popular libraries, such as Hugging Face and LangChain.

  • How to add domain knowledge and memory into LLM pipelines using embeddings and vector databases.

  • Understand the nuances of pre-training, fine-tuning, and prompt engineering, and apply that knowledge to fine-tune a custom chat model

  • How to evaluate the efficacy and bias of LLMs using different methods.

  • How to implement LLMOps and multi-step reasoning best practices for an LLM workflow.

Syllabus

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  • Module 1 - Applications with LLMs
  • Module 2 - Embeddings, Vector Databases and Search
  • Module 3 - Multi-stage Reasoning
  • Module 4 - Fine-tuning and Evaluating LLMs
  • Module 5 - Society and LLMs: Bias and Safety
  • Module 6 - LLMOps

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

This course is part of Large Language Models Professional Certificate Program

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Expert instruction
2 skill-building courses
Self-paced
Progress at your own speed
3 months
4 - 9 hours per week

Ways to take this course

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Certificate

Free

Price

$99 USD

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Expires on Jun 7

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