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Hardcore DevelopersShu-Ha-Ri Method

Production AI

Build Your Own MLOps Platform—Ship ML Reliably at Scale

Stop using AWS SageMaker's APIs. Build your own MLOps platform instead. The ONLY masterclass teaching production ML infrastructure from Kubernetes orchestration to automated deployment—own your platform, stop renting from AWS, Google, and managed services.

87% of ML models never make it to production—they rot in Jupyter notebooks because data scientists don't understand Docker, Kubernetes, or deployment pipelines. This masterclass teaches you to build production-grade MLOps platforms from scratch—capable of Kubernetes orchestration, automated Kubeflow pipelines, MLflow experiment tracking, BentoML model serving, and Evidently drift detection. You won't rely on AWS SageMaker, Google Vertex AI, or any managed platform—you'll build the infrastructure yourself: containerization with Docker, orchestration with Kubernetes, CI/CD pipelines, feature stores, and production monitoring.

This is not another course on using managed ML platforms or clicking through cloud consoles. This is executive technical education (Harvard/MIT/Stanford caliber) merged with a masterclass for tech founders and ML platform engineers. Using the DrLee.AI Shu-Ha-Ri learning method, you'll go from notebook scientist to production ML architect in 9 transformative modules.

Each module begins with a TedTalk-style presentation on MLOps architecture, then you immediately build it yourself with hands-on coding. You'll containerize ML applications, deploy to Kubernetes clusters, orchestrate training pipelines with Kubeflow, serve models with BentoML, and monitor everything with Prometheus and Grafana—not just configure cloud services.

Different from using AWS SageMaker/Google Vertex AI: While managed platforms abstract away the complexity, this course teaches you to build the MLOps infrastructure yourself—own the deployment pipelines, monitoring systems, feature stores, and automation workflows. When your models fail at 2am, you'll know exactly why and how to fix it. Platform users are commoditized. Infrastructure builders command $250K+ salaries.

By the end, you won't just understand how production ML works—you'll own production-ready MLOps infrastructure serving millions of predictions per day that becomes your competitive moat.

FROM
Notebook Scientist
$100K-$150K · Models rot in Jupyter
TO
ML Platform Engineer
$200K-$350K · Production ML at Scale
9 modules · 45 hours · Build MLOps platforms serving millions of predictions/day with 99.9% uptime
Start Your TransformationSee The Journey
The Production ML 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 notebook-only data scientist to ML platform engineer, building your production infrastructure moat with every step.

Weeks 1-3

PHASE 1: Foundation Infrastructure

MLOps Foundations, Kubernetes & Feature Engineering

FROM
“Our ML works locally but we can't deploy it anywhere—DevOps teams reject our code as 'not production-ready'”
TO
“We've built a complete Kubernetes-based ML infrastructure from scratch with experiment tracking, feature stores, and container orchestration”
🛡️ Production Infrastructure Mastery
Only 5% of ML engineers can build Kubernetes-based ML platforms from scratch. This foundational capability separates you from notebook-only data scientists.
Weeks 4-6

PHASE 2: Pipeline Automation

Kubeflow Orchestration, Deployment & Data Engineering

FROM
“Deploying a model takes our team 3 weeks of manual work—no automation, no consistency, constant failures”
TO
“We deploy ML models to production automatically in under an hour using Kubeflow pipelines and BentoML serving”
🛡️ End-to-End Automation
Automated ML deployment worth $500K/year in velocity. Companies that ship models daily outcompete those waiting weeks for manual deployment.
Weeks 7-9

PHASE 3: Operational Excellence

Training at Scale, Validation & Production Monitoring

FROM
“Our models degrade silently and we find out from angry users—no monitoring, no drift detection, no observability”
TO
“We monitor drift in real-time, retrain automatically, and maintain 99.9% uptime with comprehensive Prometheus/Grafana/Evidently monitoring”
🛡️ Netflix-Level ML Reliability
Production operations at scale—the capability to serve millions of predictions per day reliably while competitors' models break in production.

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 platform-dependent data scientists into ML platform engineers who own their production infrastructure.

1

Module 1: Production ML Fundamentals & The MLOps Lifecycle

FROM (Point A)
“I understand ML algorithms but have no idea how to deploy them to production—my models work in notebooks but DevOps rejects them”
TO (Point B)
“I've built the complete MLOps lifecycle from data ingestion through monitoring, understand maturity levels, and can design production workflows from first principles”
🛡️ Strategic MLOps architecture knowledge—you understand WHEN and HOW to apply production practices, separating you from 90% of data scientists who only know algorithms
2

Module 2: Containerization & Kubernetes Orchestration

FROM (Point A)
“I've heard of Docker and Kubernetes but never used them—deploying my Python code to a server feels like black magic”
TO (Point B)
“I've built complete Kubernetes applications from scratch, can write Dockerfiles, deploy to clusters, manage networking, orchestrate with Helm, and implement CI/CD”
🛡️ Container orchestration expertise—the foundation of modern ML infrastructure that 85% of data scientists lack entirely
3

Module 3: Experiment Tracking & Feature Engineering

FROM (Point A)
“I run hundreds of experiments and lose track of results—I re-engineer the same features across projects because there's no central repository”
TO (Point B)
“I've implemented MLflow for complete experiment tracking and model registry, built Feast feature stores providing consistent features across training and serving”
🛡️ Reproducible ML systems with centralized feature management—infrastructure that prevents data leakage and ensures consistency worth millions in avoided failures
4

Module 4: Workflow Orchestration with Kubeflow

FROM (Point A)
“My ML pipelines are scripts I run manually in order—failures mean starting from scratch, no visibility into what's running”
TO (Point B)
“I've built complete Kubeflow pipelines orchestrating data preprocessing, training, validation, and deployment with automatic retries and visual DAG monitoring”
🛡️ Workflow orchestration at scale—automated pipelines that would cost $200K/year in engineering time to build and maintain manually
5

Module 5: Model Deployment & Serving Infrastructure

FROM (Point A)
“I train models but deploying them as APIs is a multi-week DevOps nightmare—scaling is manual, latency is unpredictable, rollbacks break everything”
TO (Point B)
“I've deployed production ML serving with BentoML providing <50ms latency, automatic scaling, and instant rollbacks, integrated with MLflow model registry”
🛡️ Production model serving expertise—the capability to serve millions of predictions per day reliably, worth $300K/year in platform costs avoided
6

Module 6: Production Data Engineering for ML

FROM (Point A)
“Data preparation is a tangled mess of notebooks—data quality issues break training, I can't efficiently pass datasets between pipeline stages”
TO (Point B)
“I've built production data pipelines with Kubeflow notebooks, MinIO object storage, data quality checks, and reusable preprocessing components”
🛡️ Production data engineering for ML—the capability to process terabytes of data reliably for model training, worth $250K/year in data engineer hiring costs
7

Module 7: Distributed Training Pipelines

FROM (Point A)
“Training takes days on my laptop—I can't utilize GPUs efficiently, hyperparameter tuning is manual, failed runs waste hours of compute”
TO (Point B)
“I've built distributed training pipelines on Kubernetes with GPU scheduling, automated hyperparameter search, TensorBoard monitoring, and fault tolerance”
🛡️ Distributed training infrastructure—the capability to train models 100x faster than laptop-bound data scientists, worth $400K/year in compute optimization
8

Module 8: Advanced Training & Model Validation

FROM (Point A)
“Model validation is running accuracy on a test set and calling it done—I don't understand domain-specific metrics or proper validation strategies”
TO (Point B)
“I've implemented comprehensive model validation with domain-specific metrics, stratified splitting, automated model comparison, and seamless MLflow registry integration”
🛡️ Rigorous validation infrastructure—preventing bad model deployments that could cost millions in business impact, while accelerating iteration 5x
9

Module 9: Monitoring, Drift Detection & Explainability

FROM (Point A)
“Models degrade silently over months—users complain before we know there's a problem, no drift detection, no explainability, no alerting”
TO (Point B)
“I've implemented comprehensive ML monitoring with Prometheus, Evidently drift detection, explainability tools, and alerting—99.9% uptime with proactive issue detection”
🛡️ Production ML observability—Netflix-level reliability worth millions in prevented downtime and customer trust

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 Production ML Sovereignty Stack™ and built production MLOps platforms

📈 Career Transformation

82%
Promoted to ML Platform Engineer within 12 months
$100K-$200K
Average salary increase (MLOps premium)
93%
Report deployment capabilities as career differentiator
88%
Lead production ML initiatives after completion

💰 Business Impact

$300K-$800K
Annual MLOps platform cost elimination
87%
Eliminate AWS SageMaker/Vertex AI dependencies
78%
Raise funding with production ML infrastructure moat
2-5 months
Average time to ROI on MLOps 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 Production ML Sovereignty Stack™. Choose based on your learning style and goals.

Self-Paced Mastery

$997
Lifetime Access
Self-directed learners
  • All 9 modules (45+ hours of video)
  • Complete code repositories for every module
  • Downloadable infrastructure templates (Kubernetes YAML, Helm charts)
  • Lifetime access to all content and updates
  • Private Discord community access
  • Monthly group Q&A sessions (recorded)
  • Certificate of completion
Most Popular

9-Week Live Cohort

$3,997
12 Weeks
Engineers wanting accountability
  • Everything in Self-Paced PLUS:
  • 9 live weekly sessions (3 hours each) with Dr. Lee
  • Live coding demonstrations and Q&A
  • Weekly homework with personalized code review
  • Private cohort-only Slack channel
  • 1:1 office hours (30 minutes, 2x per cohort)
  • Graduation project: deploy your own production ML system
  • Job search support (resume review, interview prep) for engineers
  • Investor pitch support (technical slides, architecture diagrams) for founders
  • Lifetime access to all future cohort recordings

Founder's Edition (1:1 Implementation)

$19,997
6 Months
Founders & technical leaders
  • Everything in Bootcamp PLUS:
  • 12 weeks of 1:1 implementation support (2 hours/week, 24 hours total)
  • Custom ML platform architecture design for your organization
  • Technology stack selection consulting
  • Infrastructure cost optimization analysis
  • Hiring/team building guidance (what roles to hire, when)
  • Code review of your production systems (unlimited during 12 weeks)
  • Strategic consulting on ML platform roadmap
  • Investor presentation support (technical architecture slides)
  • Quarterly check-ins for 1 year post-program
  • Private advisory board access (quarterly meetups)

5-Day Intensive Bootcamp

Everything in Cohort PLUS:. 5 consecutive days, 8 hours/day (40 hours total). Intensive hands-on implementation (70% coding, 30% instruction).

Course Curriculum

9 transformative steps · 45 hours of hands-on content

1

Module 1: Production ML Fundamentals & The MLOps Lifecycle

7 lessons · Shu-Ha-Ri cycle

  • Executive Overview: Why 87% of ML Projects Never Reach Production
  • The Complete ML Lifecycle: From Data Collection to Continuous Monitoring
  • Skills Bridging Data Science and Infrastructure Engineering
  • Build vs. Buy Decision Framework for ML Platforms
  • MLOps Maturity Assessment: Level 0 to Level 2 Progression
  • DevOps vs. MLOps: Why ML Requires Different Infrastructure
  • Tools and Infrastructure Stack Overview: Kubernetes, Kubeflow, MLflow, BentoML
2

Module 2: Containerization & Kubernetes Orchestration

9 lessons · Shu-Ha-Ri cycle

  • Docker Fundamentals: Writing Dockerfiles for ML Applications
  • Building and Optimizing Docker Images for Production
  • Kubernetes Architecture Deep Dive: Clusters, Nodes, Pods, and Services
  • Kubectl Mastery: Managing Kubernetes from Command Line
  • Kubernetes Objects: Deployments, Services, ConfigMaps, Secrets
  • Networking and Service Discovery for ML Workloads
  • Helm Charts: Package Management and Infrastructure as Code
  • CI/CD for ML: GitLab CI and Argo CD Implementation
  • Prometheus and Grafana: Infrastructure Monitoring Stack
3

Module 3: Experiment Tracking & Feature Engineering

8 lessons · Shu-Ha-Ri cycle

  • MLflow for Complete Experiment Tracking: Parameters, Metrics, Artifacts
  • Data Exploration and Analysis Best Practices
  • MLflow Model Registry: Versioning, Staging, and Production Promotion
  • Feast Feature Store: Registering and Managing Features
  • Feature Retrieval: Online vs. Offline Feature Stores
  • Real-Time Feature Serving with Feast Server
  • Feast UI: Feature Discovery and Governance
  • Integrating Experiment Tracking with Feature Engineering Workflows
4

Module 4: Workflow Orchestration with Kubeflow

8 lessons · Shu-Ha-Ri cycle

  • Why Pipeline Orchestration is Critical for Production ML
  • Kubeflow Architecture: Components, Pipelines, and Workflows
  • Building Modular Pipeline Components with Clear Input/Output Contracts
  • Creating ML Pipeline DAGs: Dependency Graphs and Parallel Execution
  • Data Passing Strategies: Small Values vs. Large Datasets
  • Building an Income Classifier Pipeline from Scratch
  • Pipeline Monitoring: Tracking Execution and Debugging Failures
  • Reusable Component Libraries for Team Collaboration
5

Module 5: Model Deployment & Serving Infrastructure

9 lessons · Shu-Ha-Ri cycle

  • Why Model Deployment is Hard: Challenges and Solutions
  • BentoML Service Architecture: Services and Runners
  • Building Bentos: Packaging Models for Production Deployment
  • Loading Models with BentoML Runner from MLflow Registry
  • Deploying Bentos to Kubernetes at Scale
  • Model Serving Optimization: Latency, Throughput, and Batching
  • BentoML with MLflow Integration: End-to-End Workflow
  • KServe Alternative: When to Use Different Serving Platforms
  • Evidently for Data Drift Monitoring and Detection
6

Module 6: Production Data Engineering for ML

8 lessons · Shu-Ha-Ri cycle

  • Launching Kubeflow Notebook Servers with Custom Environments
  • Workspace and Data Volume Management for Collaboration
  • Creating Custom Notebook Docker Images with Dependencies
  • Efficient Data Passing: Simple Values, Paths, and Artifacts
  • MinIO S3-Compatible Object Storage for Training Data
  • Data Quality Validation and Early Failure Detection
  • Project: Data Preparation Pipeline for Object Detection
  • Project: Data Preparation Pipeline for Movie Recommender
7

Module 7: Distributed Training Pipelines

8 lessons · Shu-Ha-Ri cycle

  • GPU Resource Management and Scheduling in Kubernetes
  • Training on Custom Datasets: Data Loading and Preprocessing
  • Model Checkpointing and Fault Tolerance for Long Training Runs
  • TensorBoard Integration: Real-Time Training Visualization
  • Automated Hyperparameter Optimization with Kubeflow Katib
  • Building Modular Training Components for Multiple Architectures
  • Training Object Detection Models with YOLO on Custom Data
  • Downloading and Managing Data with MinIO in Training Pipelines
8

Module 8: Advanced Training & Model Validation

8 lessons · Shu-Ha-Ri cycle

  • VolumeOp for Persistent Data Storage Across Pipeline Runs
  • Advanced Data Splitting: Time-Based, Stratified, and K-Fold Strategies
  • Domain-Specific Metrics: Precision, Recall, F1, AUC-ROC, Business KPIs
  • MLflow Experiment Comparison: Analyzing Metrics Across Runs
  • Model Registry Lifecycle Management: Staging Gates and Approvals
  • Pre-Production Inference Testing: Validating Models Before Deployment
  • Creating Training and Validation Kubeflow Components
  • Building Complete Training Pipelines with Automated Validation
9

Module 9: Monitoring, Drift Detection & Explainability

8 lessons · Shu-Ha-Ri cycle

  • Basic Monitoring with Prometheus: Request Rates, Latency, Errors
  • Custom ML Metrics: Prediction Distribution, Confidence Scores, Feature Statistics
  • Centralized Logging Infrastructure for Distributed ML Systems
  • Alerting Strategies: When to Notify Teams of Production Issues
  • Evidently Drift Detection: Automated Data and Model Drift Monitoring
  • Building Drift Detection Dashboards and Alerting Pipelines
  • Model Explainability: SHAP, LIME, and Domain-Specific Techniques
  • Capstone: Your Complete MLOps Platform 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 ML engineers build production-grade MLOps platforms from scratch—from Kubernetes orchestration to automated deployment—so they can command $200K-$350K roles as ML infrastructure architects without being dismissed as 'notebook scientists who can't ship to production.'

For Founders & CTOs

I help technical founders build production MLOps platforms that eliminate $300K-$800K/year in hiring costs and create defensible infrastructure moats, so they can raise Series A with 'we ship ML reliably at scale' positioning without hearing 'your models aren't production-ready' from every technical investor.

DockerKubernetesKubeflowMLflowBentoMLEvidentlyFeastArgo CDPrometheusGrafana

Frequently Asked Questions

Is this just for LLMs or all ML models?

The MLOps principles and infrastructure work for any ML model—traditional classifiers, deep learning models, or LLMs. You'll build pipelines for object detection and recommendation systems, and the patterns apply to any model type.

Do I need Kubernetes experience?

No. We teach Kubernetes from the ground up, including Docker fundamentals. By the end, you'll be comfortable deploying and managing ML systems on Kubernetes.

What if my company uses different tools?

The concepts transfer across tools. We teach with Kubeflow, MLflow, and BentoML, but the patterns—experiment tracking, pipeline orchestration, model serving, drift detection—apply to any MLOps stack.

What hardware do I need?

A standard laptop for development. We provide cloud setup instructions for running Kubernetes clusters. Local development uses Minikube or similar.

Will I build something that actually works?

Yes. You'll build complete pipelines for object detection and movie recommendation—from data preparation through training, deployment, and monitoring. Real projects, not toy examples.

What's the business case for MLOps?

MLOps is the difference between models that sit in notebooks and models that generate business value. Proper infrastructure reduces time-to-production, improves reliability, and enables the continuous improvement loop that makes ML valuable.

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
$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