Large Language Models: Application through Production
Offered By: Databricks via edX
Course Description
Overview
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!
Syllabus
- 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
Taught by
Matei Zaharia, Sam Raymond, Chengyin Eng and Joseph Bradley
Related Courses
Hugging Face on Azure - Partnership and Solutions AnnouncementMicrosoft via YouTube Question Answering in Azure AI - Custom and Prebuilt Solutions - Episode 49
Microsoft via YouTube Open Source Platforms for MLOps
Duke University via Coursera Masked Language Modelling - Retraining BERT with Hugging Face Trainer - Coding Tutorial
rupert ai via YouTube Masked Language Modelling with Hugging Face - Microsoft Sentence Completion - Coding Tutorial
rupert ai via YouTube