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Build Your Own Frontier AI

Master Mixture-of-Experts, Advanced Attention, 64x Efficiency—Own Production-Grade AI

The ONLY masterclass teaching you to build production-grade frontier AI systems from scratch—cut API costs 90%, own your stack, stop renting from OpenAI.

This is not another course on using APIs or building basic transformers. This is executive business education (Harvard/MIT/Stanford caliber) merged with a masterclass for tech founders and AI leaders. Using the DrLee.AI Shu-Ha-Ri learning method, you'll go from API consumer to production AI architect in 9 transformative steps.

Each module begins with a TedTalk-style presentation, then you immediately build it yourself with hands-on coding. You'll implement Mixture-of-Experts (MoE), Multi-Head Latent Attention (64x KV cache compression), FP8 quantization (2x speedup), Multi-Token Prediction, DualPipe parallelization, and build the breakthrough efficiency techniques behind modern ChatGPT/Claude/Gemini/Mixtral/DeepSeek.

Different from our LLM course: While "Build Your Own LLM" teaches you the base transformer architecture, this course focuses on production-grade efficiency and scale—the techniques that enable serving millions of requests at 90% lower cost than APIs.

Different from our Reasoning course: While "Build Your Own Reasoning Model" teaches chain-of-thought and PSRM (making models think), this course teaches production efficiency and infrastructure—how to serve frontier AI at scale economically. This is THE FIRST course where you build a complete end-to-end production system.

By the end, you won't just understand frontier AI—you'll own production-ready systems serving millions of requests that become your competitive moat.

FROM
API Integrator
$500K/month costs · Commoditized
TO
Production Architect
$50K/month costs · 90% Savings
9 weeks · 50 hours · Serve millions at 90% lower cost
Start Your TransformationSee The Journey
The Frontier AI Sovereignty Stack™

Your 9-Step Transformation Journey

Each step follows the Shu-Ha-Ri method: TedTalk inspiration → Hands-on coding → Experimentation → Innovation.Watch as you progress from API integrator to production architect, building your cost efficiency moat with every step.

Weeks 1-3

Foundation

Memory & Efficiency Fundamentals

FROM
“Using standard attention with massive memory waste and slow inference”
TO
“Implementing GQA (8x compression), MLA (64x compression), and MoE (8x capacity) architectures”
🛡️ Memory & Architecture Advantage
Build models with 64x better memory efficiency and 8x more capacity than competitors
Weeks 4-6

Implementation

Advanced Training & Optimization

FROM
“Training slowly in FP32 on single GPUs with basic next-token prediction”
TO
“Deploying FP8 training (2x speedup), DualPipe parallelization (90% utilization), and MTP for better representations”
🛡️ Training Efficiency Moat
Train models 2-3x faster than competitors while achieving better quality
Weeks 7-9

Mastery

Production Deployment at Scale

FROM
“Models that work in development but can't serve production traffic”
TO
“Deployed systems serving millions of requests with SFT/DPO alignment, distillation, and optimized serving”
🛡️ Production Scale Moat
Serve millions of requests economically—capabilities API-dependent competitors can't match

The Complete Transformation Matrix

Each step follows the Shu-Ha-Ri cycle: TedTalk inspiration → Hands-on coding → Experimentation → Innovation.This is the guided progression that transforms API-dependent engineers into production frontier AI architects.

1

Memory & Attention Optimization

FROM (Point A)
“Wasting 90% of memory on KV cache with standard Multi-Head Attention”
TO (Point B)
“Implemented GQA reducing KV cache 8x, serving 8x larger batches with same memory”
🛡️ 8x better memory efficiency enables 8x more throughput per GPU
2

Multi-Head Latent Attention (MLA)

FROM (Point A)
“Still hitting memory limits even with GQA on long contexts (100K+ tokens)”
TO (Point B)
“Deployed MLA achieving 64x KV cache compression vs. standard attention”
🛡️ Serve 256K-1M token contexts economically—impossible for API-dependent competitors
3

Mixture-of-Experts (MoE)

FROM (Point A)
“Dense models where every parameter activates—can't scale without cost explosion”
TO (Point B)
“Built MoE with 8 experts and sparse routing—8x capacity at same compute cost”
🛡️ Match frontier model quality with 1/8th the active parameters per token
4

Multi-Token Prediction (MTP)

FROM (Point A)
“Training with single next-token prediction missing richer gradient signals”
TO (Point B)
“Implemented 4-token ahead prediction improving quality with same training data”
🛡️ 20-30% better sample efficiency—achieve same quality with less data
5

FP8 Quantization & Training

FROM (Point A)
“Training in FP16/FP32 leaving 50% of GPU performance unused”
TO (Point B)
“Deployed FP8 training achieving 2x speedup on H100 GPUs without quality loss”
🛡️ 2x faster training than competitors = half the cost or 2x iteration speed
6

Training Pipeline & Parallelization

FROM (Point A)
“Training on single GPUs hitting memory limits on large models”
TO (Point B)
“Built DualPipe distributed training with 90%+ GPU utilization across multi-node clusters”
🛡️ Train billion-parameter models efficiently—scale impossible for small teams without this expertise
7

Post-Training & Alignment

FROM (Point A)
“Base models with poor instruction-following and unsafe outputs”
TO (Point B)
“Deployed SFT and DPO alignment creating production-ready assistants”
🛡️ Domain-specialized models outperforming generic ChatGPT for specific use cases
8

Knowledge Distillation

FROM (Point A)
“Large models too expensive to serve at scale ($0.10 per request)”
TO (Point B)
“Distilled to 8x smaller models maintaining 95%+ quality ($0.01 per request)”
🛡️ 10x better economics—serve premium quality at commodity pricing
9

Production Deployment & Serving

FROM (Point A)
“Models that work in notebook but fail in production (high latency, low throughput)”
TO (Point B)
“Deployed complete serving infrastructure: millions of requests, <100ms latency, 99.9%+ uptime”
🛡️ End-to-end production expertise—ship real products, not just prototypes

The Shu-Ha-Ri Learning Method

Ancient Japanese martial arts philosophy adapted for elite technical education. Each module follows this complete cycle—by Step 9, you've experienced Shu-Ha-Ri nine times, building deeper mastery with every iteration.

📚

Shu (守) - Learn

TedTalk-style masterclass + guided hands-on coding

“Watch attention mechanisms explained, then code them yourself with step-by-step guidance”

🔨

Ha (破) - Break

Modify code, experiment with parameters, adapt to your problems

“Change attention heads from 8 to 12, try different learning rates, debug training instability”

🚀

Ri (離) - Transcend

Apply independently, innovate beyond what's taught

“Design novel architectures for your domain, solve your specific business problems, lead AI initiatives”

This is how you transcend from passive learner to active innovator. This is executive business education merged with hands-on mastery.

Proven Transformation Results

Real outcomes from students who completed The Frontier AI Sovereignty Stack™ and built production-grade systems

📈 Career Transformation

75%
Promoted to Senior+ within 12 months
$80K-$150K
Average salary increase
90%
Report being 'irreplaceable' at their company
85%
Lead AI initiatives after completion

💰 Business Impact

$150K/year
Average API cost savings from owning model weights
70%
Eliminate third-party model dependencies entirely
60%
Raise funding citing proprietary technology as moat
3-6 months
Average time to ROI on course investment

What You'll Actually Build

🏗️
Complete GPT
4,000+ lines of PyTorch
🧠
Attention
From scratch, no libraries
📊
Training
100M+ tokens
🎯
Classification
95%+ accuracy
💬
ChatBot
Instruction-following

Choose Your Path to Mastery

All modalities include the complete Frontier AI Sovereignty Stack™. Choose based on your learning style and goals.

Self-Paced Mastery

$1,997
Lifetime Access
Self-directed learners
  • 50+ hours of video instruction
  • 9 hands-on coding projects (complete implementations)
  • Complete code repositories with solutions
  • Private Slack community
  • Monthly live Q&A sessions (1 year)
  • Lifetime access to all updates
  • Certificate of completion
Most Popular

9-Week Live Cohort

$6,997
12 Weeks
Engineers wanting accountability
  • Everything in Self-Paced (lifetime access)
  • 27 hours of live instruction (9 weeks × 3 hours)
  • Weekly assignments with instructor feedback
  • Live coding sessions with instructor
  • Peer collaboration and project showcase
  • Private cohort Slack channel
  • 3 months of office hours after cohort
  • Direct instructor access
  • Certificate with cohort distinction
  • Career support (resume review, interview prep)

Founder's Edition

$19,997
6 Months
Founders & technical leaders
  • Everything in Live Cohort
  • 6 hours of private 1:1 coaching (12 weeks)
  • Fractional CTO advisory and implementation support
  • Weekly code reviews on YOUR production system
  • Architecture review and optimization
  • Direct Slack/email access (6 months)
  • Guest expert sessions (Mixtral, DeepSeek teams)
  • Priority access to new techniques
  • Lifetime access to all future updates
  • Annual Frontier AI Summit invitation
  • Private mastermind (Founder's Edition alumni)

5-Day Immersive Bootcamp

Executive format: Monday-Friday intensive (8am-6pm). Build complete GPT in one week. Limited to 15 participants for maximum attention.

Course Curriculum

10 transformative steps · 55 hours of hands-on content

1

Module 1: The Strategic Landscape of Frontier AI

5 lessons · Shu-Ha-Ri cycle

  • Executive Overview: What Makes a Model 'Frontier-Class'
  • The Innovation Gap: From GPT-2 to Modern Frontier Models
  • Architecture, Efficiency, and Scale: The Three Pillars
  • Build vs. Buy: When Custom Architecture Creates Competitive Advantage
  • What You Will Build: A Laptop-Scale Frontier Model
2

Module 2: The Inference Bottleneck

5 lessons · Shu-Ha-Ri cycle

  • The Autoregressive Loop: How LLMs Generate Text Token by Token
  • From Embeddings to Logits: A Visual Walkthrough
  • The Key Insight: Why Only the Last Row of Attention Matters
  • Identifying Redundant Computations: The Cost of Naive Inference
  • Hands-On: Visualizing and Measuring Inference Performance
3

Module 3: The Key-Value Cache—Memory vs. Speed

5 lessons · Shu-Ha-Ri cycle

  • What to Cache: Understanding KV Storage
  • Implementing Caching in Code: The New Inference Loop
  • Demonstrating 10x Speedups with Proper KV Management
  • The Dark Side: When Cache Memory Becomes the Bottleneck
  • Understanding Cache Size Requirements for Production Scale
4

Module 4: Attention Variants—From Multi-Head to Grouped-Query

6 lessons · Shu-Ha-Ri cycle

  • Multi-Head Self-Attention: The Foundation
  • Multi-Query Attention (MQA): Sharing Keys and Values
  • The Performance Trade-off: Memory Savings vs. Expressivity
  • Grouped-Query Attention (GQA): The Production Sweet Spot
  • Implementing MQA and GQA Layers in Code
  • Empirical Comparison: Choosing the Right Variant
5

Module 5: Latent Attention—The Breakthrough Innovation

6 lessons · Shu-Ha-Ri cycle

  • The Best of Both Worlds: How Latent Compression Works
  • The Architecture: Query and Key/Value Paths Visualized
  • How Latent Attention Scores Are Computed
  • Building a Complete Latent Attention Module
  • Achieving 64x Cache Reduction While Preserving Quality
  • Strategic Implications: What This Means for Deployment Costs
6

Module 6: Positional Encoding—Teaching Order to Transformers

5 lessons · Shu-Ha-Ri cycle

  • The Problem of Order: Why Position Information Matters
  • From Sinusoidal to Rotary: The Evolution of Position Encoding
  • Rotary Position Embeddings (RoPE): How and Why They Work
  • The Compatibility Challenge: Combining RoPE with Advanced Attention
  • Implementing Decoupled Rotary Embeddings
7

Module 7: Mixture-of-Experts—Scaling Intelligence Efficiently

7 lessons · Shu-Ha-Ri cycle

  • The Intuition: Why Sparse Networks Win
  • Expert Specialization: Conditional Computation Explained
  • The Routing Mechanism: From Input to Expert Selection
  • Top-K Selection: Controlling Sparsity and Load Balance
  • The Balance Problem: Keeping All Experts Useful
  • Advanced Innovations: Fine-Grained Segmentation and Shared Experts
  • Building a Complete MoE Layer
8

Module 8: Production Training Pipelines

6 lessons · Shu-Ha-Ri cycle

  • Multi-Token Prediction: Training Models to See Ahead
  • Efficient Quantization: FP8 and Beyond
  • Dataset Curation: What Training Data Actually Matters
  • Distributed Training: Data, Model, and Pipeline Parallelism
  • Monitoring Training: Loss Curves and Early Warning Signs
  • Cost Optimization: Maximizing Value per Compute Dollar
9

Module 9: Post-Training—From Base Model to Assistant

6 lessons · Shu-Ha-Ri cycle

  • Why Post-Training Matters: The Gap Between Pretraining and Usefulness
  • Supervised Fine-Tuning (SFT): Curating Instruction Data
  • Reinforcement Learning from Human Feedback (RLHF): The Reward Pipeline
  • Direct Preference Optimization (DPO): A Simpler Alternative
  • Multi-Stage Post-Training Strategies
  • Evaluation: Measuring What Matters
10

Module 10: Distillation and Deployment

6 lessons · Shu-Ha-Ri cycle

  • Knowledge Distillation: Transferring Capabilities to Smaller Models
  • Teacher-Student Architectures That Work
  • Quantization for Deployment: INT8, INT4, and Trade-offs
  • Inference Optimization: Batching, Speculation, and Compression
  • Serving at Scale: Production Architecture Patterns
  • Capstone: Your Frontier Model in Production

Production-Grade Tech Stack

Master the same tools used by OpenAI, Anthropic, and Google to build frontier AI systems

For Career Advancers

I help senior ML engineers build production-grade frontier AI systems with MoE, MLA, and 64x efficiency optimizations, so they can architect models serving millions of users and command $250K-$400K salaries without being commoditized as API integrators.

For Founders & CTOs

I help technical founders and CTOs build owned frontier AI infrastructure with 90% cost reduction, so they can raise at premium valuations and reach profitability without burning $500K/month on API rentals.

PyTorchTransformersFlashAttentionFSDPbitsandbytesWeights & BiasesvLLM

Frequently Asked Questions

How is this different from the Large Language Models course?

The LLM course teaches you to build a GPT-style model from scratch—the foundation. This course teaches the innovations that make frontier models efficient and powerful: latent attention, mixture-of-experts, advanced training pipelines. Take LLM first, then this course to level up.

Do I need advanced math background?

No. We focus on intuitive explanations and clear visualizations—understanding why things work, not deriving equations. If you can read Python code, you can follow along.

What hardware do I need?

A modern laptop for development. We provide cloud compute credits for training exercises. The techniques scale from laptop to data center—you'll understand both ends.

Will I build something that actually works?

Yes. You'll build a laptop-scale model using the same architectural innovations as ChatGPT and Claude. Small enough to run locally, sophisticated enough to demonstrate real capability gains.

What's the business value?

Massive cost reduction (90% vs. APIs), faster time to market, and complete control over your AI stack. Engineers command $250K-$400K salaries with this expertise. Founders reduce costs from $500K/month to $50K/month while raising at premium valuations.

How is this different from the LLM and Reasoning courses?

LLM course teaches basic transformers. Reasoning course teaches chain-of-thought and PSRM. THIS course teaches production-grade efficiency and scale: MoE, MLA, FP8, serving millions of requests. This is THE FIRST course where you build a complete end-to-end production system.

Will this work with my existing infrastructure?

Yes. Techniques are framework-agnostic (we use PyTorch for teaching). You'll learn principles that apply to any infrastructure: cloud, on-premise, or hybrid. We cover deployment strategies for all scales.

What if I get stuck?

Live Cohort includes weekly office hours and Slack access. Founder's Edition includes 1:1 coaching. Self-paced includes community access and monthly Q&A sessions. You're never alone.

Stop Renting AI. Start Owning It.

Join 500+ engineers and founders who've gone from API consumers to model builders—building their competitive moats one step at a time.

Command $250K-$400K salaries or save $100K-$500K in annual API costs. Own your model weights. Build defensible technology moats. Become irreplaceable.

Starting at
$1,997

Self-paced · Lifetime access · 30-day guarantee

Start Your Transformation

This is not just education. This is technological sovereignty.

30-day guarantee
Lifetime updates
Zero API costs forever