Cohort 1

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

Damien Benveniste

Build Production-Ready LLMs From Scratch

Medallia

Rackspace Technology

Bluestem Brands

Dell

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

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

Who is this course for

What you’ll get out of this course

<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

Provide a brief description for each module as a way to sell the value of what students will be learning throughout the course.

First Module

Second Module

Third Module

Course syllabus

What people are saying

Meet your instructor

Live sessions - every session will be recorded and available after

Saturdays & Sundays

Hands-on projects with community support for maximum learning

Weekly projects

Course schedule

This course builds on live workshops and hands-on projects

Active hands-on learning

You’ll be interacting with other learners through breakout rooms and project teams

Interactive and project-based

Join a community of like-minded people who want to learn and grow alongside you

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