LLM Ops - Large Language Models in Production

4.9

(23 ratings)

4 Weeks

Cohort-based Course

Cohort 1 has been open-sourced @ bit.ly/llmops1 LLM Ops has expanded into The AI Engineering Bootcamp @ bit.ly/AIEbootcamp

LLM Ops - Large Language Models in Production

4.9

(23 ratings)

4 Weeks

Cohort-based Course

Cohort 1 has been open-sourced @ bit.ly/llmops1 LLM Ops has expanded into The AI Engineering Bootcamp @ bit.ly/AIEbootcamp

Hosted by

Dr. Greg Loughnane and Chris "The LLM Wizard 🪄" Alexiuk

LLM Educators @ AI Makerspace and @ FourthBrain (Funded by Andrew Ng's AI Fund)

Dr. Greg Loughnane and Chris "The LLM Wizard 🪄" Alexiuk

LLM Educators @ AI Makerspace and @ FourthBrain (Funded by Andrew Ng's AI Fund)

Course overview

Become the AI Engineer that every company needs

Future-proof your career with AI engineering. Design, assemble, and operate production LLM applications with industry-standard tools.

Data science and ML skills that were relevant just a few years ago are being outpaced by AI. It is now possible to build what used to take months in a single day!

This course is designed to help you think at the right level of abstraction about AI, and to write code that will help you prototype and ship production LLM applications at the leading edge.

Who is this course for

Advanced developers with an understanding of fundamental ML concepts and the product development process

Experienced data scientists or machine learning engineers who want to build production LLM applications

Software engineers who want to get up to speed on Generative AI and explore new career opportunities

Advanced developers with an understanding of fundamental ML concepts and the product development process

Experienced data scientists or machine learning engineers who want to build production LLM applications

Software engineers who want to get up to speed on Generative AI and explore new career opportunities

What you’ll get out of this course

Engineer LLM apps from scratch with Python and using LLM Ops frameworks including LangChain and LlamaIndex

Build, deploy, evaluate, and improve production Retrieval Augmented Generation (RAG) Question Answering systems

Master key LLM product development techniques like prompt engineering for retrieval, caching, and versioning

Learn to leverage Agents across LLM Ops frameworks using Reasoning-Action (ReAct) logic

Work with other talented builders to bring your LLM project or startup idea to life

This course includes

Interactive live sessions

Lifetime access to course materials

15 in-depth lessons

Direct access to instructor

7 projects to apply learnings

Guided feedback & reflection

Private community of peers

Course certificate upon completion

Maven Satisfaction Guarantee

This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.

Course syllabus

Expand all modules

Week 1
Feb 13—Feb 18
Week dates are set to instructor's time zone
Modules
🧑‍💻 Building Large Language Model Applications in Pure Python
🧑‍💻 Building Large Language Model Applications in Pure Python
0 Lessons • 0 Projects
Session 1: Intro to LLM Ops
Session 1: Intro to LLM Ops
2 Lessons • 1 Project
Session 2: Retrieval Augmented Generation (RAG) 101
Session 2: Retrieval Augmented Generation (RAG) 101
1 Lesson • 1 Project
Week 2
Feb 19—Feb 25
Week dates are set to instructor's time zone
Modules
Session 3: Intro to LangChain
Session 3: Intro to LangChain
2 Lessons • 1 Project
Session 4: Evaluation of RAG Applications
Session 4: Evaluation of RAG Applications
2 Lessons • 1 Project
Week 3
Feb 26—Mar 3
Week dates are set to instructor's time zone
Modules
Session 5: Deploying Open-Source RAG Applications to Production
Session 5: Deploying Open-Source RAG Applications to Production
2 Lessons • 1 Project
Session 6: Intro to LlamaIndex
Session 6: Intro to LlamaIndex
2 Lessons • 1 Project
Week 4
Mar 4—Mar 7
Week dates are set to instructor's time zone
Modules
Session 7: Agents
Session 7: Agents
3 Lessons • 1 Project
Session 8: Demo Day and Graduation!
Session 8: Demo Day and Graduation!
1 Lesson

4.9

(23 ratings)

What students are saying

LLM Application Learning Outcomes

Scores represent students self-reported abilities before and after taking the course (n=27)!

Prerequisite

Be able to leverage a standard Interactive Development Environment (IDE) for building LLM software

Tools include VS Code, UNIX terminal, Jupyter Notebooks, and Conda package management.

🔗 Resource Links: Video, Code

Be able to programmatically access Open AI API as a developer

🔗 gpt-3.5-turbo, gpt-4 links: Video, Code

🔗 gpt-4-turbo, DALL·E 3 links: Video, Code

Free resource

📅 Detailed Schedule!

Understand how everything comes together in the course to provide a holistic overview of the LLM Ops space. Get all the details about the assignments and tool stacks!

Send me the deets ✌️

What people are saying

Cannot recommend the instructors ENOUGH when it comes to building, shipping, and sharing LLM applications. They inspire a hands-on mentality and are thoughtful and responsive to feedback and requests. They are amazing people to have in your network and were instrumental in helping me switch careers from structural engineering to Generative AI!"

Pano Evangeliou, M.Sc.

Senior AI Engineer

My journey with LLM Ops has been transformative! The hands-on ethos, engaging community sessions, and guidance from instructors demystified LLMs for me, making app dev accessible and enjoyable. This course fueled my confidence, empowered me to bring a passion project to life, and grew my network of LLM practitioners. An inspirational adventure!

Yi Lin

Associate Manager, Digital, Analytics, and Technology

More than a course this is an experience. Under the expert guidance of the instructors I found myself going deep into ML, understanding theory, code, and application like never before. Beyond knowledge, it's about community. If you are planning to attend a live course on AI/ML, I recommend AI Makerspace as the best place to get that education.

Juan Olano

Founder, Senior AI Engineer

Dedicated to the craft. What a team of instructors! What a community that is being built from the ground, on the cutting-edge, with enthusiasm. They have attracted a special group of practitioners and are selective in the process. At the intersection of data science, machine learning engineering, and MLOps comes the current future of LLMOps.

Dr. Todd Deshane

Software Engineer

Meet your instructors

Dr. Greg Loughnane

Founder & CEO @ AI Makerspace

I've worked as an AI product manager, university professor, data science consultant, AI startup advisor, and ML researcher; TEDx & keynote speaker, lecturing since 2013.

From 2021-2023 I worked at FourthBrain (Backed by Andrew Ng's AI Fund) to build industry-leading online bootcamps in ML Engineering and ML Operations (MLOps):

🔗 Resources links: Deeplearning.ai demos, AI Makerspace demos, LinkedIn, Twitter, YouTube, Blog.

Chris "The LLM Wizard 🪄" Alexiuk

Co-Founder & CTO @ AI Makerspace

I'm currently working as the Founding Machine Learning Engineer at Ox - but in my off time you can find me creating content for Machine Learning: either for the AI Makerspace, FourthBrain, or my YouTube Channel!

My motto is "Build, build, build!", and I'm excited to get building with all of you!

🔗 Resources links: YouTube, LinkedIn, Twitter

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