Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

edX

Open Source LLMOps

Pragmatic AI Labs via edX

Overview

Experience Open Source Large Language Models (LLMs)

  • Master cutting-edge LLM architectures like Transformers through hands-on labs
  • Fine-tune models on your data with SkyPilot's scalable training platform
  • Deploy efficiently with model servers like LoRAX and vLLM

Explore the Open Source LLM Ecosystem:

  • Gain in-depth understanding of how LLMs work under the hood
  • Run pre-trained models like Code Llama, Mistral & Stable Diffusion
  • Discover advanced architectures like Sparse Expert Models
  • Launch cloud GPU instances for accelerated compute

Guided LLM Project:

  • Fine-tune LLaMA, Mistral or other LLMs on your custom dataset
  • Leverage SkyPilot to scale training across cloud providers
  • Containerize your fine-tuned model for production deployment
  • Serve models efficiently with LoRAX, vLLM and other open servers
  • Build powerful AI solutions leveraging state-of-the-art open source language models. Gain practical LLMOps skills through code-first learning.

Syllabus

Week 1: Getting Started with Open Source Ecosystem

  • Introduction to popular open source natural language processing models and their capabilities

  • Accessing pre-trained NLP models using libraries like HuggingFace Transformers

  • Using large language models for synthetic data augmentation to enhance datasets

  • Building real-world NLP solutions using open source tools in Python and Rust

Week 2: Using Local LLMs from LLamaFile to Whisper.cpp

  • Key components of LLamaFile for packaging language models into portable files

  • Running local language models from LLamaFile on your own devices

  • Automating speech recognition workflows using Whisper.cpp

  • Integrating Whisper.cpp into GenAI building blocks and applications

Week 3: Applied Projects

  • Using language models in the browser with Transformers.js and ONNX

  • Exporting models to the ONNX format for enhanced portability

  • Developing portable command-line interfaces with the Cosmopolitan project

  • Building a phrase generator application as a native binary using Cosmopolitan

Week 4: Recap and Final Challenges

  • Connecting to local language models with APIs using Python

  • Retrieval augmented generation using local LLMs

  • Hands-on labs for GPU-accelerated MLOps workflows

  • Final project to build an interactive LLamaFile sandbox

By the end of this course, learners will have gained practical experience leveraging state-of-the-art open source language models to build AI applications. They will be able to deploy solutions on their own devices as well as integrate models into efficient MLOps pipelines.

Taught by

Alfredo Deza and Noah Gift

Reviews

Start your review of Open Source LLMOps

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.