6 Weeks
·Cohort-based Course
A 6‑week, live bootcamp for ML engineers to architect, fine‑tune, and deploy scalable LLM applications through six real‑world projects
6 Weeks
·Cohort-based Course
A 6‑week, live bootcamp for ML engineers to architect, fine‑tune, and deploy scalable LLM applications through six real‑world projects
Previously at
Course overview
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 & 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 & 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 & Recorded Content: Reinforce, Deepen, Accelerate
✨ 12 Interactive Live Workshops (3 hrs each): Each session follows the Concept → 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.
01
Senior ML Engineers upgrading to LLMs, frustrated by prototypes that crumble under real‑world load and runaway inference costs
02
Data Scientists moving into LLM workflows, stalled by opaque fine-tuning pipelines and multi‑week integration headaches.
03
Recent CS Graduates specializing in LLMs, eager to move beyond theory but lacking hands‑on experience
Deliver Six Production‑Grade LLM Artifacts
Tackle Real‑World LLM Challenges
Spend dedicated lab sessions wrestling with production‑scale puzzles, OOMs, tail‑latency spikes, and integration snags, until you persevere and ship battle‑tested solutions.
Master Core Concepts in Live Code‑Along Workshops
12 interactive sessions where you’ll implement each module live, ask questions, troubleshoot your project code, and internalize battle‑tested best practices.
Reinforce with On‑Demand Deep‑Dive Lectures
10+ hours of short videos covering Transformer math, ZeRO strategies, vLLM optimizations, and more. Complete with annotated notebooks and slides.
Peer & Instructor Code Reviews
GitHub‑style feedback rounds on your project submissions
Lifetime Access & Private Community
Revisit recordings anytime, join a Slack of LLM engineers, share new tactics, and continue growing long after the cohort wraps.
Live sessions
Learn directly from Damien Benveniste in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Build Production-Ready LLMs From Scratch
12 live sessions • 64 lessons • 6 projects
Jul
12
Architecture Basics
Jul
13
Implement The Transformer Architecture From Scratch
Jul
19
Pre-training - Supervised Learning Fine Tuning - RLHF
Jul
20
Training Implementation
Jul
26
CPU vs GPU vs TPU - The GPU Architecture - Distributed Training
Jul
27
Data Parallelism - Model Parallelism - Zero Redundancy Optimizer Strategy
Aug
2
The different fine-tuning learning tasks - Catastrophic forgetting
Aug
3
LoRA and QLoRA Adapters
Aug
9
The Deployment Strategies - Multi-LoRA - The Text Generation Layer
Aug
10
Streaming Applications - Continuous Batching - KV-Caching - The Paged-Attention - vLLM
Aug
16
Implementing and Optimizing a Retriever Augmented Generation (RAG) pipeline
Aug
17
Productionizing a RAG Pipeline with FastAPI
Welcome, my name is Damien Benveniste! After a Ph.D. in theoretical Physics, I started my career in Machine Learning more than 10 years ago.
I have been a Data Scientist, Machine Learning Engineer, and Software Engineer. I have led various Machine Learning projects in diverse industry sectors such as AdTech, Market Research, Financial Advising, Cloud Management, online retail, marketing, credit score modeling, data storage, healthcare, and energy valuation. Previously, I was a Machine Learning Tech Lead at Meta on the automation at scale of model optimization for Ads ranking.
I am now training the next generation of Machine Learning engineers.
Join an upcoming cohort
Cohort 2
$1,500
Dates
Payment Deadline
4-6 hours per week
Saturdays & Sundays
2:00pm - 5:00pm PST
Live sessions - every session will be recorded and available after
Weekly projects
2 hours per week
Hands-on projects with community support for maximum learning
The Attention Is All You Need: The Full Guide
This is a complete guide on the original Transformer architecture and attention mechanisms! This includes 40 pages of every detail you need to know to start your journey in the world of Large Language Models.
Get the Guide
Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you
Sign up to be the first to know about course updates.
Join an upcoming cohort
Cohort 2
$1,500
Dates
Payment Deadline