Build Production-Ready LLMs From Scratch

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.