LLM Ops - Large Language Models in Production


(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

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

What you鈥檒l 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鈥檚 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鈥擣eb 18

    Week dates are set to instructor's time zone


    • 馃鈥嶐煉宦燘uilding Large Language Model Applications in Pure Python

    • Session 1: Intro to LLM Ops

    • Session 2: Retrieval Augmented Generation (RAG) 101

  • Week 2

    Feb 19鈥擣eb 25

    Week dates are set to instructor's time zone


    • Session 3: Intro to LangChain

    • Session 4: Evaluation of RAG Applications

  • Week 3

    Feb 26鈥擬ar 3

    Week dates are set to instructor's time zone


    • Session 5: Deploying Open-Source RAG Applications to Production

    • Session 6: Intro to LlamaIndex

  • Week 4

    Mar 4鈥擬ar 7

    Week dates are set to instructor's time zone


    • Session 7: Agents

    • Session 8: Demo Day and Graduation!


(23 ratings)

What students are saying

LLM Application Learning Outcomes

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


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.

馃敆 Resource Links: Video, Code

Be able to programmatically access Open AI API as a developer

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

馃敆 gpt-4-turbo, DALL路E 3 links: Video, Code

Free resource

馃搮 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 鉁岋笍

What people are saying

聽聽聽聽聽聽聽聽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.

Pano Evangeliou, M.Sc.

Senior AI Engineer
聽聽聽聽聽聽聽聽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

Yi Lin

Associate Manager, Digital, Analytics, and Technology
聽聽聽聽聽聽聽聽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

Juan Olano

Founder, Senior AI Engineer
聽聽聽聽聽聽聽聽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

Dr. Todd Deshane

Software Engineer

Meet your instructors

Dr. Greg Loughnane

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):

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

Chris "The LLM Wizard 馃獎" Alexiuk

Chris "The LLM Wizard 馃獎" 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!

馃敆 Resources links: YouTube, LinkedIn, Twitter

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Course schedule

10 hours/week
  • Class!

    Tuesdays & Thursdays, 4:00-6:00pm PT

    • Nov 28th, 30th
    • Dec 5th, Dec 7th
    • Dec 12th, Dec 14th
    • Dec 19th, Dec 21st

    * All sessions will be recorded for your viewing pleasure!

  • Programming Assignments

    2-4 hours per week

    Each class period, we will get hands-on with Python coding homework!

  • LLM Ops Hack Week

    Mon, Dec 18 | Wed Dec 20th | 3-6 PM PT

    • Monday: Team formation, project ideation, storytelling workshop, system diagram presentation
    • Wednesday: Build 馃彈锔, ship 馃殺, and share 馃殌 a RAG system with at least the following components: caching, evaluation, visibility聽
  • LLM Ops Demo Day

    Thursday, December 21 at 4:00 PM PT

    • Open to the pubic - Friends, family, coworkers and bosses welcome!
  • Office Hours

    Tuesdays and Fridays

    • Greg Loughnane: Mondays @ 8 AM PT
    • Chris Alexiuk: Fridays @ 3 PM PT

Build. Ship. Share.

Build. Ship. Share.

Get hands-on with code, every class

We're here to teach concepts plus code. Never one or the other.

Pair programming made fun and easy

Grow your network. Build together. Feel the difference that expert facilitation makes.

Meet your accountability buddies

Join a community of doers, aimed in the same career direction as you are.

Frequently Asked Questions

What happens if I can鈥檛 make a live session?
I work full-time, what is the expected time commitment?
What鈥檚 the refund policy?
What if I'm not yet proficient in Python and the foundations of Machine Learning?
What if I don't know Git or how to use GitHub?
How can I learn more about AI Makerspace?
Are there any volume discounts if I want my whole team to take the course?
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