AI Makerspace

Cohort 1

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LLMO4

AIE1

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LLMO3

LLMO1

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AAIE1

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Self-paced cohort

AIE4

AIE5

Cohort 3

Cohort 4

AIE6

Cohort 8

Cohort 9

Cohort 7

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!

Become an AI Engineer in the world's leading community for people who want to build, ship, and share production LLM applications 🏗️🚢🚀.

"Dr. Greg" Loughnane

Co-Founder & CEO @ AI Makerspace

Chris "The Wiz 🪄" Alexiuk

Co-Founder & CTO @ AI Makerspace

The AI Engineering Bootcamp

Product Management

Principal - Team Lead (ML and Algorithms)

Senior Data Scientist, Product Analytics

Principal Design Engineering Specialist 

Engineering Mindset Coach

Customer Engineer

Fullstack Software Engineer

Data Scientist

Principal Engineer 

🧑‍💻 ON CODING AS AN AI ENGINEER- Do you code every day?- If not, are you committed to coding every day as an AI Engineer? If the answer to both of these questions is "No," this class is not for you. 💼 ON LEADING AI ENGINEERING TEAMS- Do you manage a team of people that writes code every day? If "Yes," this class might be for them. ------ **KEY CURRICULUM UPDATES FOR COHORT 8** 🟢 [New Additions]- Context Engineering&nbsp;from first principles- A&nbsp;primary cohort use case&nbsp;that builds on itself and runs throughout the cohort- Expansion of Model Context Protocol (MCP) content- Using Agent2Agent (A2A) Protocol to&nbsp;build and run two agents, one w/ MCP server and one to&nbsp;mimic a human using our agent- Dedicated Guardrails session, from best-practices to implementation 🔴 [Deprecated]- Explicit teaching of code and coding agents (in favor of a more implicit approach of using Claude Code regularly)- Computer Use agents (in favor of A2A and operating an Agent in production)- Contextual Retrieval- Hugging Face smol agents ------ OUR COMMUNITY🏗️ 🚢🚀 AI Makerspace is on a mission to create the world's leading community for people who want to build, ship, and share production LLM applications. Welcome! We invite you to join our close-knit AI Makerspace community on YouTube (learn live weekly with us for free at 10 AM PT on Wednesdays!) or Discord to start building, shipping, and sharing with us today! https://aimakerspace.io/ OUR COURSEThe AIE Bootcamp is truly a bootcamp. It's hard. It's a grind. But it will transform you! It will get you comfortable thriving at "The LLM Edge" and beyond. Our students don't just learn; they transform! Plus, every cohort culminates in a live Demo Day experience! Many of our graduates have launched entirely new startups based on their demo day presentations! To hear our student's stories and for a quick glance at all the tools you'll master, check out the following link now! https://aimakerspace.io/the-ai-engineering-bootcamp/ OUR ETHOSBuild 🏗️, Ship 🚢, Share 🚀! This is the secret to success in a 21st-century workforce, and you will live this every class, every week of the cohort! 🧑‍🤝‍🧑 Specifically, you will get hands-on experience building and sharing complex LLM and agentic applications weekly, receiving constant feedback during live sessions and through your homework submissions via instructors, peer supporters, and your fellow cohort members. 🙋 WHAT ARE THE PREREQUISITESYou must be familiar with Python programming, prompt engineering, and data science. Learn more about prerequisites here: https://bit.ly/aie7-prereqs 🙋 How will I know if it's right for me? If you're unsure if this course be a fit for you, you'll find tons of resources on this page to help you decide, including: 📜 Curriculum &amp; Detailed Schedule: https://bit.ly/aie7-curr🤩 Demo Day Videos (from cohort 5): https://bit.ly/aie5-demos🎤 Student Interview Videos: https://aimakerspace.io/category/transformations/ Here are a few long-form pieces of writing about the student experience:- Richard Gower, Cohort 3: https://bit.ly/aie-cohort-3-reflections- Ches Roldan's "20 Tips to Survive AI Makerspace’s AI Engineering Bootcamp (as a non-technical person)": https://bit.ly/aie-20tips 💰 How much do the tools cost? Don't worry! This course is for the GPU-poor among us! This course adapts to industry-standard open-source software and compute hardware and is cloud platform agnostic. We strive to keep your additional costs low (below ~$100 total). Check Q&amp;A for more info!** WHO HAS TAKEN THIS COURSESince its launch in January 2024, over 250 people have taken the course! The most successful participants have been Software Engineers and Developers or Data Scientists and Machine Learning/AI Specialists, indicating a focus on analytics, modeling, and artificial intelligence. Here is a list of companies whose employees have already taken the course: Google, Meta, Microsoft, Amazon, Samsung, Salesforce, IBM, Oracle, Bloomberg, Deloitte, Atlassian, SAP, Infosys, ServiceNow, Shopify, NetApp, Autodesk, Jio Platforms, JP Morgan Chase (JPMC), Walmart, Chevron, Expedia, NTT Data, Ernst &amp; Young, Fannie Mae, National Grid, State Farm, USAA, BMO, TD Bank, Mercer, Kiewit, BP, CVS, Safran, Appian The most important thing for anyone taking the course is to take it seriously. This advanced course requires commitment, debugging, resilience, and tenacity. These personal attributes matter much more than your current company or job title. Whether you're coming from a software engineering or data science background, to be a world-class AI Engineer, you'll need to know both. This class will teach you the lego blocks of building production-grade LLM applications software as an AI-Assisted Developer. This is where your journey begins! Top 10 direct quotes from Cohort 5 “One of the best lessons I’ve ever had. The instructor’s level of expertise is high and his communication is extremely easy to understand. I have grasped all the concepts.”“This session was pure gold, seeing actual applications, really nice!”“I am having lots of fun and learning. LOVE THE ENERGY! Let’s go.”“The breakout rooms are absolutely fantastic. Peer Supporters are bae.”“The breakout rooms are just so good – easily sets it apart from 90% of other courses out there.”“It was a great session today. Super helpful and getting clearer. Amazing job everyone. Love this!!!!”“I have deployed and built a number of applications right from the first sessions of the course. Knowledge also helps me in self-study and research.”“This class was worth every dollar, thank you guys!”“Thank you for an amazing ride! &lt;3”“I complained last time about the breakout rooms not being long enough and the lectures being too long. This time I think the ratio was GLORIOUS PERFECTION!” Top 10 direct quotes from Cohort 6 “I am grateful to be part of this cohort… Huge thanks to all of you!!!”“I will be taking a lot of classes async… The recordings are posted so promptly and the Discord conversations stay active — exactly what you’d expect from a high-quality course!”“I feel we’re the luckiest group to have Don and Laura in the same cohort — different styles, complementary strengths, and both incredibly patient.”“Peer groups are awesome for discussion — the chats were fire.”“Program is great. It moves fast, but I’d feel ripped off if I were too comfortable!”“Great class, like always — really enjoying the cohort so far.”“Shout out to everyone in AIM, including the peer supporters. What a great (but challenging) experience!”“The sessions are going great. I’m learning a lot, and the peer supporters (Andre &amp; Nithin) are so knowledgeable.”“Mike is doing an excellent job — we’re really grokking the content in the breakouts!”“Andre is amazing — super knowledgeable and incredibly patient in explaining everything to us.”

Prototype agent apps and get them into Production. Ship fast and keep data safe.

Course syllabus

Tuesdays & Thursdays

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

Weekly Programming Projects

Open to the public - Friends, family, coworkers, and bosses welcome!

Demo Day!

Instructor Office Hours

We typically cover 6-7 days per week with 1 or more office hours held by peer supporters!

Peer Supporter Office Hours

Course schedule

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

Data scientists and ML engineers/specialists who want to build, deploy, operate, and improve LLM apps in production environments.

Who is this course for

What you’ll get out of this course

A vibe coder won't take your job. An AI-assisted developer will.

🧑‍💻 Become an AI-Assisted Developer

Work with certified AI Engineers in dedicated small Journey groups to discuss and code throughout the cohort!

🧑‍🤝‍🧑 Peer-Supported Live Coding

You will present a unique project live to a cohort of your peers and the public!

🤩 Demo Day!

We're always at <a href="https://newsletter.aimakerspace.io/" rel="noopener noreferrer" target="_blank">The LLM Edge</a>&nbsp; with the latest concepts, code, tools, techniques, and best practices!

🕴️ industry Leading Curriculum

Certified AI Engineers get direct access to job opportunities from our network

💼 Hiring Opportunities

🧑‍💻 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)

Class!

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

Office Hours

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!

Context Engineering for Agents & Multi-Agent Systems

Dr. Greg

Co-Founder/CEO @ AI Makerspace

The Wiz

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

Not just prompt engineering, but also RAG! Learn why this fundamentally makes intuitive sense for Generative AI

Context Engineering is the new...

Enhancing search and retrieval is the primary job of agents with access to tools (and MCP servers!) today

How agents with tools help us to enhance context

How tools like Deep Research offer insights into using parallelization to further scale context optimization

How multi-agent systems enhance context

Building Agentic Systems with Protocols: MCP & A2A

How to set up an MCP Server, and why you would want to.

Model Context Protocol (MCP)

How to build a multi-agent application using A2A, and why you would want to.

Agent2Agent Protocol (A2A)

Developer vs. consumer products, RAG vs. agentic apps, prototyping vs. production, internal vs. external, etc!

Where AI Engineers should use MCP and A2A today

Long-Context: RAG is Dead, Long Live RAG

Co-Founder & CEO of AI Makerspace

Chris "The LLM Wizard 🪄" Alexiuk

Co-Founder & CTO of AI Makerspace

Do these two options accomplish the same goal?  How do we make performance versus cost tradeoffs between them?

Long-context LLMs vs. RAG

Evaluation for long-context LLMs is an emerging discipline.  Meet "Needle in a Haystack," "Ruler," and what's next!

How do we even know if long-context LLMs are good?

Technically, how was this done?  What goes into this computationally and what are the implications for LLM development?

How did Gradient increase Llama 3 8B from 8k context to 4M?

Multi-Agent Systems for Everyone

By combining prompt engineering, RAG, fine-tuning, and agents, we can understand multi-agent systems

Multi-agent workflows are just another pattern

When should you use multiple agents?  When do you not need to?

Single-agent vs. multi-agent use cases

Combine multi-agent collaboration with agent supervision to coordinate between pilot, copilot, and grounded operators.

Build a multi-agent pilot training simulator

Understand GenAI in 3 Key Patterns

Learn how RAG is Dense Vector Retrieval (think recommender systems) + In-Context Learning (think prompt engineering).

Retrieval Augmented Generation, or RAG

Learn how to think about efficient fine-tuning of Large Language Models (LLMs) and Embedding models!

Fine-Tuning

Understand the most popular buzzword in 2024 - Agents!  What is an agent, and how can we leverage them?

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