AI Makerspace

Cohort 1

Cohort 2

LLMO4

AIE1

AIE2

LLMO3

LLMO1

LLMO2

AAIE1

AIE3

AIE4

AIE5

Cohort 3

Cohort 4

AIE6

Self-paced

AIEO3

AIE7

AIEO1

AIE11

AIE10

AIE8

AIE12

AIEO4

AIEO2

AIE9

If you'd like more detail than what's on Maven, including specific reading recommendations and tooling specifications for every session, check out the curriculum &amp; detailed schedule! If you'd rather read the Cliff Notes, check out our <a href="https://aimakerspace.io/the-aie-bootcamp-changelog/" rel="noopener noreferrer" target="_blank">curriculum changelog</a> to see how things have evolved since Cohort 9 of <a href="https://maven.com/aimakerspace/ai-eng-bootcamp" rel="noopener noreferrer" target="_blank">The AI Engineering Bootcamp</a>.

The AI Engineer Certification v1.0 curriculum & detailed schedule 

Get&nbsp;all the details&nbsp;about tools we'll use and relevant papers for each project assignment, as well as our thoughts on who the ideal student is for this course, what success looks like, and the only prerequisites you'll need.

📅 Detailed Course Schedule & Curriculum

Bypass this entire course by following our step-by-step tutorial to build, ship, and share your first LLM application on your own! For FREE. Then decide if you could benefit from some community-supporting learning to fill in the details!

Take The AI Engineer Challenge

Get&nbsp;all the details&nbsp;about tools we'll use and relevant papers for each project assignment, how to become AI-Makerspace Certified, and prerequisite learning pathways.

📅 Detailed Course Schedule & Curriculum - Updated for Cohort 8!

 
 

Access all of the concepts and code directly&nbsp;on <a href="https://www.youtube.com/playlist?list=PLrSHiQgy4VjGQohoAmgX9VFH52psNOu71" rel="noopener noreferrer" target="_blank">YouTube</a>&nbsp;to start building, shipping, and sharing with LLM Ops! **Note: This is from cohort 1 which we ran between August and September of 2023** <ol><li>Build complex apps with LLM Ops frameworks including LangChain and LlamaIndex.</li><li>Understand LLM product development methods, from prompting to retrieval and beyond.</li><li>Build, deploy, evaluate, and operate your own end-to-end production RAG system.</li></ol>

Learn LLM Ops for Free, Async!

Master language models and embedding models through training, fine-tuning, aligning, distilling, and merging transformer architectures! Get access to lectures, code repositories, and more!&nbsp; We highly recommend all students taking The AI Engineering Bootcamp to go through this material first! Why? It turns out that it's super helpful to understand what's happening inside of the LLM when you try to build complex applications with them. Not required, mind you, but super helpful. Get started building, shipping, and sharing for free now 👇

LLM Engineering, Cohort 3 Full Course ~ a $1999 value 

Do you want to really understand how LLMs work "under the hood," from&nbsp;concepts&nbsp;to&nbsp;code Everyone drives cars, but how many people really know how the engine works? Whether you're looking to nail AI Engineer interviews or lead an entire AI Engineering team,&nbsp;positioning yourself the LLM expert in your context is just five days away.

LLM Foundations, a FREE 5-Day Email Course

Understand how everything comes together in the course to provide a holistic overview of the how LLMs are engineered. Get all the details about the assignments, associated papers, and key concepts you'll learn!

📅 Detailed Schedule!

You’ll find brief introductions to core concepts we’ll cover in more depth in the class - this is meant to get the learning juices flowing with bite-sized training materials from Dr. Greg and The Wiz.

The LLM Engineering Onramp

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!

📅  Detailed Schedule!

To build AI agents that create real value for people and companies. Without the fluff.

CEO @ AI Makerspace

Dr. Greg Loughnane, Ph.D.

Training the workforce of the future

AI & OSS @ NVIDIA

Chris "The Wiz 🪄" Alexiuk

Obsessed with ML, AI, & LLMs

The AI Engineering Bootcamp

Computational Linguist

Product Engineer

Directory, Software Engineering

Unity C# developer

Staff Data Engineer

Software Engineer, Machine Learning

Engineering Manager

AI Engineer

Director of Engineering

FourthBrain

University of Dayton

Wright State University

NVIDIA

** We have continued on our 🚩 mission to create the industry-standard certification for people who want to become AI Engineers with <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/aimakerspace/ai-engineer-certification">The AI Engineer Certification</a>.**-- Background --This course started in January 2024 as the next evolution of <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/aimakerspace/llmops">LLM Ops, Cohort 4</a>.Two years later, Cohort 9 (v0.9) marked the end of the next chapter.Check out the <a target="_blank" rel="noopener noreferrer nofollow" href="https://aimakerspace.io/the-aie-bootcamp-changelog/">curriculum changelog</a> from Cohorts 1-9 and track what our <a target="_blank" rel="noopener noreferrer nofollow" href="https://aimakerspace.io/graduates/">certified grads</a> are doing today. Ask them about their journeys!Free versions of our content are available on <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.youtube.com/channel/UCbDZFHUjTCCUKyXgcp3g50Q">YouTube</a> and <a target="_blank" rel="noopener noreferrer nofollow" href="https://github.com/AI-Maker-Space?view_as=public">GitHub</a>.-- Course Info --This course was designed for backend engineers who code every day and have decided it's time to go all in on building production AI applications.In just 10 weeks of building 🏗️, shipping 🚢, and sharing 🚀, you supercharge your abilities.TL;DR v0.9: Agentic AI<ul><li>🕴️Agentic RAG (2 wks)</li><li>🧰 Complex Agents (2 wks)</li><li>📊 Evals (1.5 wks)</li><li>🚀 Certification Challenge (1.5 wks)</li><li>🚢 Production Deployments (2 wks)</li><li>🤩 Demo Day (1 wk)</li></ul>Learn more here: <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.notion.so/The-AI-Engineering-Bootcamp-Course-Schedule-Curriculum-v0-9-2b1cd547af3d8075a0a4f67621f18898#2b1cd547af3d814f95b6cfa6cd8b811f">detailed curriculum</a>.

The bootcamp for professionals, NOT beginners.

🤖 Become fluent in Agent Engineering

🔤 Learn to engineer and optimize Context

🦾 Understand the foundational Concepts and Code of AIE

📈 To play the game of AI Engineering in the market

🧑‍💻 Actually learn to code AI apps; our Certification demands competence

Software engineers and developers who want to build, deploy, operate, and improve LLM apps in production environments.

The very best ML engineers who are pseudo-software engineers and have not lived in notebooks, and who want to build, deploy, ... etc.

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.

Become the AI Engineer that every company needs

Course syllabus (download the detailed schedule for the entire syllabus!)

<ul><li>Nov 28th, 30th</li><li>Dec 5th, Dec 7th</li><li>Dec 12th, Dec 14th</li><li>Dec 19th, Dec 21st</li></ul> * All sessions will be recorded for your viewing pleasure!

Class!

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

Programming Assignments

<ul><li>Monday: Team formation, project ideation, storytelling workshop, system diagram presentation</li><li>Wednesday: Build 🏗️, ship 🚢, and share 🚀 a RAG system with at least the following components: caching, evaluation, visibility&nbsp;</li></ul>

LLM Ops Hack Week

<ul><li>Open to the pubic - Friends, family, coworkers and bosses welcome!</li></ul>

LLM Ops Demo Day

<ul><li>Greg Loughnane: Mondays @ 8 AM PT</li><li>Chris Alexiuk: Fridays @ 3 PM PT</li></ul>

Office Hours

Course schedule

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

Who is this course for

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

Cohort 1 has been open-sourced  @ bit.ly/llmops1

LLM Ops has expanded into The AI Engineering Bootcamp @ bit.ly/AIEbootcamp

Dr. Greg Loughnane

Founder & CEO @ AI Makerspace

Chris "The LLM Wizard 🪄" Alexiuk 

Co-Founder & CTO @ AI Makerspace

🧑‍💻 Language Model Engineering refers to the evolving set of best practices for training, fine-tuning, and aligning LLMs to optimize their use and function to balance performance with efficiency. These best practices have formed the basis for the LLMs, or Large Language Models (a.k.a. Foundation Models) and Small Language Models (SLMs) of today. 🤖 Whether you’re looking at OpenAI’s GPT series, Anthropic Claude, Grok, Google Gemini, Mistral, or any other model provider, the core underlying transformer architectures are similar, as are the training and tuning methods. When chasing after high scores on LLM benchmarks or creating state-of-the-art SLMs, techniques like Model Merging and Distillation are important as well. 🏫 This course will provide you with the foundational concepts and code to train, fine-tune, and align state-of-the-art SLMs and LLMs using industry-standard and emerging approaches from the open-source edge heading into 2025. 🤓 Become the expert in your organization on all things training (pretraining, post-training), fine-tuning (supervised fine-tuning, instruction tuning, chat tuning, etc.), alignment (PPO, DPO, etc.), Small Language Models (SLMs), Model Merging, Distillation, and more!

Understand LLMs from first principles; build custom Small Language Models (SLMs)

Course syllabus

Weekly Programming Projects

Dr. Greg: Thursdays @ 8 AM PTThe Wiz 🪄: Fridays @ 3 PM PT

Peers supporters are here to serve as your destination group; they're available for homework help, concept deep dives, and to hang out with! Find them live in class, on Discord, and even in their own dedicated weekly sessions!

Peer Support Sessions

Data scientists, researchers, or engineers experienced with classic Machine Learning who want to build custom Small Language Models (SLMs).

Product managers who need to understand the core concepts and code behind custom SLMs to effectively lead their product teams.

Stakeholders who aim to train, fine-tune, or align proprietary SLMs for their customers in 2025.

By the end of this course, you will be able to:

🦾 Build, train, and evaluate Language Models using transformer variants (GPT, BERT, BART)

🧐  Calculate self-attention and understand the latest implementations (Flash Attention, FA2)

🔠 Demystify embedding layers, embedding representations, pre-trained vs. learned embeddings, ROPE, and more!

🪙 Decode embedding space representations for optimal next-token prediction

🔡 Build, train, and evaluate embedding models (like those in 🤗 Sentence Transformers)

🚇 Complete unsupervised and continued pretraining of LLMs and SLMs from scratch

🚉 Fine-tune pre-trained LMs for instruction-following, chat, and more via parameter-efficient methods

🛤️ Align LMs to balance helpfulness with harmlessness and other criteria (RLXF, DPO)

🚀 Explore frontiers of Language Models (Mixture-of- approaches, Model Merging, alternative fine-tuning, and more!)

Master language models and embedding models through training, fine-tuning, aligning, distilling, and merging transformer architectures!

"Dr. Greg" Loughnane

Co-Founder & CEO @ AI Makerspace

Agentic RAG from Scratch

Raul Salles de Padua

Principal AI & ML Engineer @ Rumble

Can Temizyurek

Maintainer, Evalite (>200k downloads/wk)

Meet agents through the lens of agentic RAG, based on what we learned in our Dense Vector Retrieval session!

Introduction to agents

Build durable, production-grade agents from scratch with LangGraph

Learn the core constructs of low-level orchestration

Set up tracing to observe the performance of your agents with the LangSmith platform

Understand monitoring and tracing for agents

Dense Vector Retrieval

Meet context engineering through the lense of retrieval, RAG, and the LLM application stack

Introduction to classic RAG

Understand the role that different types of LLMs play in a RAG process

Overview of embeddings and similarity search

Without using any off-the-shelf tools, build a truly Pythonic RAG application

Build a RAG application from scratch in Python

No-Code Agent Builders: OpenAI vs. LangChain

"Dr. Greg" Loughnane, Ph.D.

DL @ NVIDIA, Co-Founder & CTO @ AI Makerspace

How to build agents using our examples for either chat or visual workflow interfaces

Use No-code Agent Builders

Learn the key distinction between workflows and agents, and why it matters (and doesn't matter) for builders

Workflows vs. Agents

No-code doesn't mean no-concepts. Learn the key ideas you'll need to build your own agents.

Learn what you must understand to use Agent Builders

LangChain 1.0, or Tools for AI Engineering, Part II

Built to "ship quickly wiht standard agent patterns" and "agents that fit the default loop."

Meet LangChain 1.0

For agentic systems somewhere between workflows and agents, deep or long-running agents, and complex implementations.

Meet LangGraph 1.0

"The Platform for Agent Engineering" includes new nomenclature and abstractions from LangChain to LangGraph & LangSmith.

Understand the entire Lang-X Ecosystem

Cursor 2.0, or Tools for AI Engineering, Part I

The leading AI-IDE for AI-assisted development will look different in 2026. What does a multi-agent interface look like?

Meet the Multi-Agent Interface

Cursor has its own in-house coding model, Composer. Let's see if it's dope, or nope.

Meet Composer

What hasn't changed? Join us to learn what fundamentals and best practices remain the same from Cursor 1.0 to 2.0.

Keep the best of the classics and embrace the new

The AI Engineer Challenge: Build an End-to-End LLM App

Katerina “KatGPT” Gawthorpe, Ph.D.

Co-Founder @ eve.ai | Econometrics @ Fannie Mae | Instructor @ AI Makerspace

From version control basics to an hitting an LLM API, vibe coding a front end, and deploying to a public URL!

Zero to one with a best-practice E2E prototyping stack

Learn every steps you need to quickly spin up simple prototypes with open-source tools and without no-code solutions.

The fastest way to onramp into building and shipping apps

We will simply wrap an LLM with a front end and a back end, using a real AI Engineering development environment.

Learn directly from AI Makerspace