LLM Zoomcamp: A Comprehensive Guide to Real-Life LLM Applications
Introduction to LLM Zoomcamp
LLM Zoomcamp is an engaging and entirely free online course designed to introduce learners to the practical applications of Large Language Models (LLMs). Over the course of 10 weeks, participants are guided through the process of building an artificial intelligence system capable of effectively answering questions from a knowledge base.
How to Get Involved
To support and engage with the LLM Zoomcamp community, participants are encouraged to register on DataTalks.Club's Slack and connect through the #course-llm-zoomcamp channel. Important announcements are shared via the course's Telegram channel. Additionally, all course videos are conveniently accessible on the DataTalks.Club YouTube channel.
2024 Cohort Details
The 2024 cohort of LLM Zoomcamp kicks off on June 17. Enrollees can access materials specifically tailored for this cohort, ensuring they have the latest and most relevant resources.
Prerequisites for Participants
To make the most of the course, learners should be comfortable with programming, particularly in Python, and competent with command line interfaces and Docker. Interestingly, no previous experience with artificial intelligence or machine learning is required, widening the accessibility of the course.
A Detailed Look at the Syllabus
Pre-Course Workshops: Participants will start by implementing a search engine through hands-on workshops with both video guides and code available for reference.
1. Introduction to LLMs and RAG: Learners are introduced to basic concepts, from setting up their environment to utilizing OpenAI API and executing text searches with Elasticsearch.
2. Open-source LLMs: This module focuses on environments that support GPU, models from the HuggingFace Hub, running LLMs on CPUs, and creating simple user interfaces with Streamlit.
3. Vector Databases: Participants delve into vector searches, learning how to create and index embeddings, perform vector searches using Elasticsearch, and evaluate retrieval offline.
4. Evaluation and Monitoring: This section involves offline evaluation techniques, as well as tracking methods using metrics like Cosine and LLM-as-a-Judge. Learners also gain skills in dashboard creation using Grafana.
5. LLM Orchestration and Ingestion: Here, the focus is on data ingestion using Mage, a tool to streamline processes.
6. Best Practices: The course emphasizes improving RAG pipelines, including techniques like hybrid search, document re-ranking, and hybrid searches using LangChain.
7. Bonus Project: An optional module presents an end-to-end project example, focusing on building a fitness assistant and exploring text dataset preprocessing.
Throughout the course, there is also an opportunity for participants to engage in the LLM Zoomcamp 2024 competition, allowing them to apply their knowledge in a practical and competitive setting.
Instructors
The course is led by a team of knowledgeable instructors, including Alexey Grigorev, Magdalena Kuhn, Balaji Dhamodharan, Tommy Dang, and Timur Kamaliev, all of whom bring extensive expertise to the learning experience.
Community and Support
Participants can get support and engage with the community through DataTalks.Club’s Slack. For a more structured discussion, attendees should adhere to the provided guidelines for asking questions.
Acknowledgments
LLM Zoomcamp is made possible thanks to the support of sponsors like Mage, DLTHub, and Saturn Cloud. Their involvement supports the accessibility of education in machine learning and artificial intelligence.
By completing LLM Zoomcamp, learners will not only enrich their understanding of LLMs but also acquire practical skills that are increasingly relevant in today’s AI-driven world.