Goabiaryan

LLMOps: Roles & Career Transitions

O'Reilly Media

@Packtpub

DataCamp

ODSC

PyData

Abi Aryan

Gain a clear overview of the LLMOps ecosystem. What it is, why it matters, and how it connects to real-world AI Systems

Understand the LLMOps Landscape

Learn about the core roles like AI Engineer, LLMOps Engineer, Platform Engineer, GenAI PM etc so you know where u fit

Break Down High-Demand Roles

Get a practical framework to map your current skills to emerging LLMOps roles & create a personalized action plan

Chart Your Transition Path

🧑‍💻 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 7** 🔴 [Deprecated]- Classic LangChain RAG Chains (in favor of most up-to-date LangGraph implementation)- Fine-Tuning of LLMs &amp; Embedding Models (we now consider these techniques table stakes ... learn LLM Engineering for free @ bit.ly/llm-engineering)- Chainlit for Front Ends (no longer a best-practice tool for creating front-ends)- Hugging Face Spaces (no longer a best-practice deployment tool for quick POCs)- Hugging Face Inference Endpoints (no longer the easiest way to deploy high performance open LLMs and embedding models endpoints) 🟢 [New Additions]- A custom AI Makerspace MCP server integrated into Cursor that has all of the docs and tools that we'll use in the course!- Model Context Protocol (MCP) &amp; Agent2Agent Protocol (A2A)- Vercel for quick end-to-end POC deployments- Together.ai for quick deployment of Llama-3.3-70B-Instruct-Turbo and open embedding endpoints- A focus on vibe-coded custom front ends with React (or other) framework- OpenAI Agents SDK (released March 2025)- Code agents with Hugging Face's Smol Agents Framework (released ~Jan 2025)- CLI Coding Agents with Codex (released April 2025)- Computer Use Agents with OpenAI's Responses API (released March 2025) ------ 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! WHAT STUDENTS (REALLY) SAID ABOUT COHORT 5:We collect feedback every single class. Here is an analysis of the 160 pieces of feedback that we received on the question "Is there anything else you'd like to share with us?" that we asked at the end of each of the 20 live sessions, completed by ChatGPT with the o1 model in April, 2025. Highly Positive Themes- Students repeatedly praise the instructors’ expertise and clarity.- Many love the breakout rooms, citing them as a key differentiator from other courses.- There is strong enthusiasm about practical coding sessions, community support, and peer supporters.- Several comments mention that they are already deploying real applications and seeing immediate value, which is a significant selling point for future students. Neutral or Constructive Feedback- A few suggestions for improving logistics (e.g., link consistency, environment setup, more thorough instructions).- Requests for additional examples and smaller breakout groups.- A handful of people struggled with first-class onboarding or missed classes due to personal constraints. Negative or Dissatisfied Feedback- Some frustration around external tool links (Discord, GitHub) not working seamlessly.- Occasional complaints about environment setup challenges.- A single extended critique about missing the first day and feeling lost initially, but that student did eventually get on track. We are always working to improve our product, our student experience, and our processes! You can count on the fact that we've taken the feedback into account for Cohort 6 and beyond! Here were the top 10 direct quotes: 1. “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.”2. “This session was pure gold, seeing actual applications, really nice!”3. “I am having lots of fun and learning. LOVE THE ENERGY! Let’s go.”4. “The breakout rooms are absolutely fantastic. Peer Supporters are bae.”5. “The breakout rooms are just so good – easily sets it apart from 90% of other courses out there.”6. “It was a great session today. Super helpful and getting clearer. Amazing job everyone. Love this!!!!”7. “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.”8/ “This class was worth every dollar, thank you guys!”9. “Thank you for an amazing ride! &lt;3”10. “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!”

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

Course syllabus

<ul><li>W1: June 24th, 26th</li><li>W2: July 1st, 3rd</li><li>W3: July 8th, 10th</li><li>W4: July 15th, 17th</li><li>W5: July 22nd, 24th</li><li>W6: July 29th, 31st</li><li>W7: August 5th, 7th</li><li>W8: August 12th, 14th</li><li>W9: August 19th, 21st</li><li>W10: August 26th, 28th</li></ul> * All sessions are recorded!

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! <a href="https://www.youtube.com/playlist?list=PLrSHiQgy4VjGG6wvPyIPoBnH2iqjtLY68" rel="noopener noreferrer" target="_blank">AIE5 Demo Day YouTube Playlist</a><a href="https://www.linkedin.com/events/7305318024528478208/comments/" rel="noopener noreferrer" target="_blank">AIE5 Demo Day Event</a><a href="https://www.notion.so/AIE5-Demo-Day-Run-of-Show-1b5cd547af3d805e8bb7d049413ee129" rel="noopener noreferrer" target="_blank">AIE5 Demo Day Run of Show</a>

Demo Day!

Dr. Greg: Thursdays @ 10 AM PTThe Wiz 🪄: Fridays @ 3 PM PT

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

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

Cohort 7

"Dr. Greg" Loughnane

Co-Founder & CEO @ AI Makerspace

Chris "The Wiz 🪄" Alexiuk

Co-Founder & CTO @ AI Makerspace

AIE6

Cohort 8

If you’re a software engineer or data scientist, chances are you’ve built or seen a proof of concept (POC) for a generative AI app. It’s exciting at first—an impressive demo that showcases the potential of large language models. But here’s the problem: that’s where it usually stops. These POCs often fail to scale into reliable, production-ready applications. The result? Endless iteration cycles, unreliable outputs, and frustration as teams struggle in what we call “POC purgatory.” It doesn’t have to be this way. With the right principles, processes, and workflows, you can build LLM applications that work in production – without overcomplicating pipelines or chasing trends. This course focuses on the iterative development, evaluation, and debugging practices that make AI systems production-ready. We’ll explore tools like RAG, embeddings, and agents, but the emphasis is on building scalable, robust systems – ensuring you leave with the mindset and skills to adapt as the technology evolves. You’ll not only learn how to design, test, and deploy generative AI applications, but also how to iterate and improve rapidly. This isn’t just about building something cool – it’s about creating AI systems that solve real problems, deliver real value, and scale with confidence. What to ExpectThis is a highly interactive course designed to get you results. Across live workshops, discussions, and hands-on projects, you’ll build your skills while solving real-world challenges. •	Live coding sessions to design and refine LLM applications.•	Iteration exercises to explore how small changes can dramatically improve reliability.•	Discussions on user-centric development and integrating feedback loops.•	Expert-led Q&amp;As to help you tackle your toughest challenges. What You’ll Learn •	How to transform POC demos into production-grade applications.•	Strategies for managing non-determinism and optimizing outputs through prompt engineering.•	How to monitor, log, and debug AI systems to ensure reliability and performance.•	Best practices for handling structured outputs and integrating function calling into your applications.•	Building workflows for iterative development and experimentation.•	Practical skills through hands-on app development, including creating a Generative AI app to query PDFs. Bonus Perks All students will receive over $1,500 USD in credits! -&nbsp;$500 in Modal credits&nbsp;for cloud compute – giving you resources to power real AI applications.- $500 in Logfire credits (from the creators of Pydantic)- $300 in Google Cloud and Gemini credits for the first 100 registrants.- 6 months free Hugging Face Pro.- 6 months free Prodigy.- 3 months free Learn Prompting Plus. Guest lectures Learn directly from leading practitioners solving real AI problems in production. These sessions focus on practical workflows, debugging strategies, and building scalable systems – not just theoretical research or cutting-edge multi-agent architectures. Guest speakers in this cohort will include: - Jason Liu &amp; Ivan Leo (Instructor, 567 Labs)- Shreya Shankar (UC Berkeley, ex-Google Brain)- Zach Mueller (Hugging Face)- Paige Bailey (Google DeepMind)- Ines Montani (Explosion AI, spaCy)- Alan Nichol (Rasa)- Nathan Danielsen (Carvana)- Charles Frye (Modal)- John Berryman (Arcturus, ex-Github)- Skylar Payne (AI executive for startups. Ex-Google. Ex-LinkedIn)- Vincent Warmerdarm (marimo)- Samuel Colvin (Pydantic, Logfire) Enrolling will also give you access to all recorded guest lectures from previous cohorts of this course: - Hamel Husain (Ex-Airbnb, GitHub) – Basic Data Literacy for Debugging and Evaluating LLMs&nbsp;- Swyx (Latent Space podcast, smol.ai, ai.engineer): Engineering AI Agents in 2025- Sander Schulhoff (CEO, LearnPrompting.org) – Prompt Engineering in the LLM SDLC&nbsp;- Charles Frye (Developer Advocate, Modal) – Hardware for LLM Developers- Ravin Kumar (DeepMind) – End-to-End LLM Product Development This Course Is For •	Data scientists and machine learning engineers who are sick of unreliable POCs and want to ship reliable LLM applications.•	Software engineers who want to learn how to build generative AI systems and master the LLM software development lifecycle. This Course Is Not For Those •	Looking for an introductory course in AI—some programming knowledge is required.•	Expecting ready-made solutions for AI problems without active participation.•&nbsp;Expecting deep dives into advanced evals, multi-agent systems, or large-scale RAG pipelines. This course emphasizes building and iterating on practical, production-ready systems.•	Unable to commit to hands-on projects, live discussions, and iterative development exercises. Why Take This Course By the end of this course, you’ll: •	Understand the first principles of generative AI development.•	Gain hands-on experience building robust, scalable applications.•	Learn how to troubleshoot, monitor, and optimize AI systems for production.•	Develop workflows to iterate and refine AI applications effectively.•	Leave with the confidence to deploy reliable AI systems at scale.

Master the patterns and processes that underpin successful GenAI app development

Tuesdays and Thursdays

To ensure hands-on practical time, there will be project work to complete throughout the course.

Weekly projects

Data scientists, machine learning engineers who are sick &amp; tired of seeing and building prototypes &amp; want to ship reliable LLM applications

Software engineers who want to learn how to build Generative AI systems and learn the LLM software development lifecycle.

You’ve seen impressive generative AI demos—but how do you take them to production? In this course, you’ll learn the exact workflows, tools, and engineering practices needed to turn concepts into reliable, scalable applications that deliver real value.

Move beyond POCs and build production-ready AI systems

Struggling with inconsistent outputs? Through interactive exercises, you’ll explore proven techniques for optimizing prompts and ensuring your AI delivers actionable results in production settings.

Master prompt engineering for reliable results

When your AI application breaks, you’ll know exactly how to fix it. We’ll walk you through real-world debugging sessions, monitoring techniques, and tools to ensure your systems stay reliable at scale.

 Learn to debug and monitor AI systems like a pro

Want your AI to produce precise, usable results? You’ll learn how to handle structured outputs and function calls, making your applications smarter and more user-friendly.

Build smarter applications with structured outputs

Stop wasting time in endless iteration cycles. Through guided projects, you’ll discover how to set up workflows that refine your applications faster and with less frustration.

Accelerate your development process with iterative workflows

Tired of theoretical knowledge? You’ll build a fully functional app that uses text and image models to query PDFs, giving you practical experience solving complex real-world problems.

Develop hands-on expertise by building GenAI and LLM apps

Whether you’re looking to lead AI projects, impress stakeholders, or level up your technical expertise, this course gives you the tools to stand out as an AI/ML practitioner.

Advance your career with practical, proven techniques

These aren’t just skills for demos—they’re skills for delivering scalable, production-ready systems that solve real-world business challenges.

Solve Real Problems at Scale

Leave the course with a project you can add to your portfolio, demonstrate to your boss, or use to showcase your ability to deploy reliable AI systems.

Showcase Tangible Results

Build LLM-powered software reliably & from first principles. Learn the GenAI software development lifecycle: agents, evals, iteration & more

Hugo Bowne-Anderson

Host of Vanishing Gradients podcast; ex-Outerbounds; expert in DS/ML.

Stefan Krawczyk

Agentforce; Ex-Stitch Fix; 13+ Years building / productionizing data, ML, Agents

Cohort 2

Cohort 3

Our course is designed to train creative leaders to master AI tools and techniques - spanning across open source such as ComfyUI and commercial like Runway and ElevenLabs. Gain Industry-Leading Expertise in AIImagine becoming a creative leader who understands AI inside and out. Our course equips you with the knowledge and skills to apply ethical AI practices, legal insights, and cutting-edge tools, giving you the competitive edge to lead your team and industry. Hands-On PracticeBest way to learn is to do. We put a lot of emphasis on actual practical knowledge.Through the course you will work on a project, immediately testing and implementing the different tools and workflows we will cover. Group of Industry ExpertsOur course features a carefully curated group of industry leaders, each personally selected to ensure the highest quality of collaboration and learning.Participants come from diverse backgrounds - advertising, film production, commercial production, VFX, product design... By the end of this course, you'll not only understand the creative and technical sides of AI but also gain the leadership skills needed to guide your team through AI-driven innovation.Become the go-to expert in your field, ready to inspire and implement transformative AI solutions that will shape the future of your industry.

Lead Your Industry with AI Workflows → Become the Go-To Expert in 12 weeks.

Once per week we will do a two and a half hour masterclass with hands on project work. Covering the topic of that week.

Masterclass Thursdays

An opportunity to review the challenges you have from the previous masterclass.You can submit question in advance and we will solve them in a live session.

Q&A Wednesdays

Along with the live sessions we will provide you with additional video explanations, pdf templates and comfy workflows.

Course Materials

Talk with mentors and members of the community in a carefully selected group of top-tier creative leaders.

Community Support

Creative Directors, Art Directors looking to advance their expertise with Generative AI.

Senior Creatives working in production, advertising, design, broadcasting, visual storytelling.

Innovators and Creative Technologists looking to unlock new frameworks and designs for their projects.

Gain a deep understanding of AI’s capabilities, limitations, and emerging trends. Learn how to navigate the evolving AI landscape to stay ahead of the curve, making informed decisions about how to integrate AI effectively into your creative work.

Understanding the AI landscape - capabilities, limitations and future trends.

We’ll explore in-depth text, image, and video workflows that you can use locally with AnythingLLM and ComfyUI. You’ll learn from existing workflows and create custom ones tailored to your specific needs.

Advanced AI workflows and techniques with open-source software like ComfyUI and AnythingLLM.

You’ll get access to selected tools and platforms we use throughout the course—from workflow editors to cloud deployment services. These tools will help you build, test, and deploy your workflows.

Get Access to Software Tools We Use

Develop a forward-thinking mindset that embraces AI as a creative partner, while understanding the ethical considerations that come with new technology.

Evolve your mindset, creative process and ethics around AI.

Learn how to apply AI tools and workflows directly to the unique challenges and opportunities in your industry.

Master AI for text, image, video, sound and apply them to real world use cases.

Gain the skills and confidence to effectively teach and guide your team or students, empowering them to harness the potential of AI in their own creative work.

Ability to pass on the learning to your team and students.

Immediately apply the knowledge with real life use case project work.

You will have a full finished project applicable to your creative field by the end of the course.

Be part of a carefully selected group of top-tier creative leaders, offering unparalleled opportunities for collaboration, growth, and networking.

You will be part of a hand picked community of creative experts and leaders.

Build a production ready AI pipeline for your industry across all areas — creative process, ethics, legal, text, image, video, sound.

Nejc Susec

Luka Tisler

Cohort 1

The Real-World LLM Engineering Roadblocks You Face Today 👋 Transitioning from General ML to LLM Specialization: You’ve built recommendation engines or classifier models, but moving into Transformer‑centric development feels like learning a whole new discipline—no clear roadmap exists.👋 Lack of LLM‑Specific Career Path: You see “LLM Engineer” roles popping up on LinkedIn, but your current CV only shows “Data Scientist” or “ML Engineer.” You need hands‑on projects and artifacts to credibly make the jump.👋 Career Stalled by “Academic” Skillset: You can recite Transformer papers, but when asked, “Have you shipped an LLM feature end‑to‑end?” you have no answer—and no portfolio to prove it!👋 Prototype Meltdown Under Production Load: You’ve fine‑tuned a small model locally, but when you switch from 1 to 100 concurrent requests, your GPU memory spikes and inference grinds to a halt, because you’ve never applied continuous batching, KV caching, or paged‑attention in a live setting.👋 RAG Integration Headaches: Turning a standalone model into a live, Retriever‑Augmented Generation service becomes a multi‑week integration nightmare. How this course will help you Because we’ve packaged every stage of the LLM lifecycle, from career transition to production rollout, into a six‑week bootcamp that:✅ Guides Your Career Pivot: You’ll emerge with six polished GitHub projects, a deployment playbook, and RAG demos that transform your resume from “ML generalist” to “LLM Specialist.”✅ Attacks Each Pain‑Point Head‑On: Attacks each pain point head‑on with six job‑mirroring projects (from scratch → RLHF → scaling → deployment → RAG), so you never waste time on dead‑end tutorials✅ Live Code‑Along Workshops &amp; Office Hours: Tackle your own fine‑tuning bugs, scaling hiccups, and deployment errors alongside Damien in dedicated sessions, so you get hands‑on fixes for the exact issues you’ll face on the job.✅ Ready‑to‑Use Repos &amp; Playbooks: Grab our curated starter code, development scripts, deployment templates, and debugging checklists, so you can plug them straight into your next project without reinventing the wheel.✅ A Portfolio of Six Production‑Grade Projects: Leave with six end‑to‑end deliverables, from a Transformer built from scratch to a live RAG API, ready to showcase on GitHub, in performance reviews, or to hiring managers. No more scattered blog-hopping or generic bootcamps, this is the only cohort where you’ll master Transformer internals and ship production‑grade LLM systems while making the career leap you’ve been aiming for. What You’ll Actually Build and Ship Across six hands‑on projects, you’ll deliver deployable LLM components and applications, no fluff, just job‑ready code:✅ A Modern Transformer Architecture from scratch: Implement a sliding‑window multihead attention to slash O(N²) to O(N·w), RoPE for relative positional encoding, and the Mixture-of-Expert architecture for improved performance, all in PyTorch.✅ Instruction‑Tuned LLM: Fine‑tune a model with supervised learning, RLHF, DPO, and ORPO for instruction following on a real benchmark and compare performance gains.✅ Scalable Training Pipeline: Containerize a multi‑GPU job with DeepSpeed ZeRO on SageMaker to maximize throughput and minimize cost.✅ Extended‑Context Model: Modify RoPE scaling, apply 4/8‑bit quantization, and inject LoRA adapters to double your context window.✅ Multi‑Mode Deployment: Stand up a Hugging Face endpoint, a vLLM streaming API, and an OpenAI‑compatible server, all Dockerized and optimized for low latency.✅ End‑to‑End RAG Chat App: Build a FastAPI backend with conversational memory and a Streamlit UI for live Retrieval‑Augmented Generation. By the end of Week 6, you won’t just know these techniques, you’ll have shipped six production‑grade artifacts, each reflecting the exact pipelines, optimizations, and deployment routines you’ll use on the job. Live &amp; Recorded Content: Reinforce, Deepen, Accelerate ✨ 12 Interactive Live Workshops (3 hrs each): Each session follows the Concept&nbsp;→&nbsp;Code flow. I’ll introduce the day’s core topic (e.g. self-attention, LoRA, vLLM optimizations, ...), and we’ll implement the features step‑by‑step in code so you see exactly how theory maps to code. Bring your questions!✨ 10 + Hours of On‑Demand Deep‑Dive Lectures: Short videos (10–20 min) on Transformer internals, fine-tuning tricks, deployment optimizations. Watch before each project to hit the ground running. Step through every line of code at your own pace; perfect for review or catching up if you miss a live session. Downloadable slide decks, annotated notebooks, and cheat sheets you’ll reference long after graduation. Why This Matters: Live workshops turn recorded concepts into actionable skills. You’ll see how theory maps directly onto code, get instant feedback, and internalize best practices. Then, recorded lectures become your asynchronous safety net, letting you revisit tricky topics, prepare for upcoming labs, and solidify your understanding on demand.

From Prototype to Production: Ship Scalable LLM Systems in 6 Weeks

Live sessions - every session will be recorded and available after

Saturdays & Sundays

Hands-on projects with community support for maximum learning

Senior ML Engineers upgrading to LLMs, frustrated by prototypes that crumble under real‑world load and runaway inference costs

Data Scientists moving into LLM workflows, stalled by opaque fine-tuning pipelines and multi‑week integration headaches.

Recent CS Graduates specializing in LLMs, eager to move beyond theory but lacking hands‑on experience

<ul><li>Implement modern Transformer architecture from Scratch</li><li>Fine-tune LLMs for instruction following</li><li>Build a scalable training pipeline</li><li>Fine-tune LLM to extend context window with RoPE and QLoRA</li><li>Deploy API endpoints for production-ready LLM</li><li>Build end‑to‑end RAG chat app</li></ul>

Deliver Six Production‑Grade LLM Artifacts

Spend dedicated lab sessions wrestling with production‑scale puzzles, OOMs, tail‑latency spikes, and integration snags, until you persevere and ship battle‑tested solutions.

Tackle Real‑World LLM Challenges

12 interactive sessions where you’ll implement each module live, ask questions, troubleshoot your project code, and internalize battle‑tested best practices.

Master Core Concepts in Live Code‑Along Workshops

10+&nbsp;hours of short videos covering Transformer math, ZeRO strategies, vLLM optimizations, and more. Complete with annotated notebooks and slides.

Reinforce with On‑Demand Deep‑Dive Lectures

GitHub‑style feedback rounds on your project submissions

Peer & Instructor Code Reviews

Revisit recordings anytime, join a Slack of LLM engineers, share new tactics, and continue growing long after the cohort wraps.

Lifetime Access & Private Community

A 6‑week, live bootcamp for ML engineers to architect, fine‑tune, and deploy scalable LLM applications through six real‑world projects

Lightning Lessons

LLMOps: Roles & Career Transitions