Open Source LLMOps Solutions

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

Open Source LLMOps Solutions

Coursera · Beginner ·🧠 Large Language Models ·3mo ago
Skills: LLMOps90%

Key Takeaways

Deploys open source LLMOps solutions for large language models

Original Description

Learn the fundamentals of large language models (LLMs) and put them into practice by deploying your own solutions based on open source models. By the end of this course, you will be able to leverage state-of-the-art open source LLMs to create AI applications using a code-first approach. You will start by gaining an in-depth understanding of how LLMs work, including model architectures like transformers and advancements like sparse expert models. Hands-on labs will walk you through launching cloud GPU instances and running pre-trained models like Code Llama, Mistral, and stable diffusion. The highlight of the course is a guided project where you will fine-tune a model like LLaMA or Mistral on a dataset of your choice. You will use SkyPilot to easily scale model training on low-cost spot instances across cloud providers. Finally, you will containerize your model for efficient deployment using model servers like LoRAX and vLLM. By the end of the course, you will have first-hand experience leveraging open source LLMs to build AI solutions. The skills you gain will enable you to further advance your career in AI.
Watch on External: Coursera ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
We Built AI Using Wikipedia, But Wikipedia Is 40% Wrong
Learn how AI built using Wikipedia can be flawed due to the platform's inaccuracies and why this matters for AI development
Medium · Machine Learning
📰
I Built an LLM Filter That Prefers Silence Over Slop — and the Eval Harness That Keeps It Honest
Learn how to build an LLM filter that prioritizes silence over slop and create an evaluation harness to ensure its accuracy
Dev.to AI
📰
Open-Weight LLM API Integration: A Developer's Practical Guide
Learn to integrate open-weight LLM APIs into your applications for more flexible and customizable AI solutions
Dev.to AI
📰
Fine-Tuning: Lleva tus modelos de IA al siguiente nivel con precisión real
Learn how to fine-tune pre-trained AI models for specific business needs to achieve more precise and coherent results
Medium · Machine Learning
Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch →