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https://github.com/DataTalksClub/llm-zoomcamp

LLM Zoomcamp - a free online course about building a Q&A system
https://github.com/DataTalksClub/llm-zoomcamp

Last synced: 2 days ago
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LLM Zoomcamp - a free online course about building a Q&A system

Host: GitHub
URL: https://github.com/DataTalksClub/llm-zoomcamp
Owner: DataTalksClub
Created: 2024-03-26T14:53:20.000Z (8 months ago)
Default Branch: main
Last Pushed: 2024-09-19T14:54:45.000Z (about 2 months ago)
Last Synced: 2024-10-29T15:38:40.885Z (10 days ago)
Language: Jupyter Notebook
Homepage:
Size: 2.09 MB
Stars: 2,843
Watchers: 61
Forks: 457
Open Issues: 3
Metadata Files:
- Readme: README.md

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README

        # LLM Zoomcamp 



  



LLM Zoomcamp - a free online course about real-life applications of LLMs. In 10 weeks you will learn how to build an AI system that answers questions about your 

knowledge base.



  



- Give us a star to support the course!

- Register in [DataTalks.Club's Slack](https://datatalks.club/slack.html)

- Join the [`#course-llm-zoomcamp`](https://app.slack.com/client/T01ATQK62F8/C06TEGTGM3J) channel

- Join the [course Telegram channel with announcements](https://t.me/llm_zoomcamp)

- The videos are published on [DataTalks.Club's YouTube channel](https://www.youtube.com/c/DataTalksClub) in [the course playlist](https://www.youtube.com/playlist?list=PL3MmuxUbc_hKiIVNf7DeEt_tGjypOYtKV)

- [Frequently asked technical questions](https://docs.google.com/document/d/1m2KexowAXTmexfC5rVTCSnaShvdUQ8Ag2IEiwBDHxN0/edit?usp=sharing)

- [Course Calendar](https://calendar.google.com/calendar/?cid=NjkxOThkOGFhZmUyZmQwMzZjNDFkNmE2ZDIyNjE5YjdiMmQyZDVjZTYzOGMxMzQyZmNkYjE5Y2VkNDYxOTUxY0Bncm91cC5jYWxlbmRhci5nb29nbGUuY29t)

## 2024 cohort 

- Start date: June 17

- [Materials specific to 2024 cohort](cohorts/2024/)

 

## Pre-requisites

* Comfortable with programming and Python

* Comfortable with command line

* Docker

* No previous exposure to AI or ML is required

## Syllabus

We encourage [Learning in Public](learning-in-public.md)

### Pre-course workshops

Implement a search engine: [Video](https://www.youtube.com/watch?v=nMrGK5QgPVE), [code](https://github.com/alexeygrigorev/build-your-own-search-engine)

### 1. [Introduction to LLMs and RAG](01-intro/)

* LLMs and RAG

* Preparing the environment 

* Retrieval and the basics of search

* OpenAI API

* Simple RAG with Open AI

* Text search with Elasticsearch

### 2. [Open-source LLMs](02-open-source/)

* Getting an environment with a GPU

* Open-source models from HuggingFace Hub

* Running LLMs on a CPU with Ollama

* Creating a simple UI with Streamlit 

### 3. [Vector databases](03-vector-search/)

* Vector search

* Creating and indexing embeddings

* Vector search with Elasticsearch

* Offline evaluation of retrieval

### [Workshop: dlt](cohorts/2024/workshops/dlt.md)

### 4. [Evaluation and monitoring](04-monitoring/)

* Offline evaluation of RAG

* Cosine and LLM-as-a-Judge metrics 

* Tracking chat history and user feedback

* Creating dashboards with Grafana for visualization

### 5. [LLM orchestration and ingestion](05-orchestration/)

* Ingesting data with Mage

### 6. [Best practices](06-best-practices/)

* Techniques to improve RAG pipeline

* Hybrid search

* Document reranking

* Hybrid search with LangChain

### 7. [Bonus: End-to-End project example](07-project-example/) (Optional)

* Building an end-to-end fitness assistant project

* Examples of pre-processing text datasets 

### LLM Zoomcamp 2024 Competition

[More details](cohorts/2024/competition/)

### [Hands-on project](project.md)



  



## Instructors

- [Alexey Grigorev](https://linkedin.com/in/agrigorev/)

- [Magdalena Kuhn](https://www.linkedin.com/in/magdalenakuhn/)

- [Balaji Dhamodharan](https://www.linkedin.com/in/balaji-dhamodharan-91b45615/)

- [Tommy Dang](https://www.linkedin.com/in/dangtommy/)

- [Timur Kamaliev](https://www.linkedin.com/in/timurkamaliev/)

## Asking questions

The best way to get support is to use [DataTalks.Club's Slack](https://datatalks.club/slack.html). Join the [`#course-llm-zoomcamp`](https://app.slack.com/client/T01ATQK62F8/C06TEGTGM3J).

To make discussions in Slack more organized:

* Follow [these recommendations](asking-questions.md) when asking for help

* Read the [DataTalks.Club community guidelines](https://datatalks.club/slack/guidelines.html)

## Supporters and partners

Thanks to the course sponsors for making it possible to run this course



  

    

  





  

    

  





  

    

  



Do you want to support our course and our community? Please reach out to [[email protected]]([email protected])