Production AI
Ship Models That Last—Build Your Own MLOps Platform
90% of ML projects never make it to production. This course ensures yours does.
Delivering a successful machine learning project is hard. Most models die in notebooks. This masterclass teaches you to build a production-quality ML platform from scratch—incorporating MLOps and DevOps with a stack of proven infrastructure tools. You'll design reliable systems that streamline data workflows, improve collaboration between data and operations teams, and provide the structure needed for both training and deployment. Whether you're deploying traditional models or frontier LLMs, this course provides the crucial MLOps framework to get them into production and keep them running.
Your Competitive Moat
Proven Transformation Results
Real outcomes from students who completed The LLM Sovereignty Stack™ and built their competitive moats
📈 Career Transformation
💰 Business Impact
What You'll Actually Build
Choose Your Path to Mastery
All modalities include the complete LLM Sovereignty Stack™. Choose based on your learning style and goals.
Self-Paced Mastery
- All 9 steps available immediately
- 50 hours of content + code
- Lifetime access to updates
- Community support
- Monthly live office hours
- 30-day money-back guarantee
9-Week Live Cohort
- Everything in Self-Paced
- Weekly live workshops (2 hrs)
- Direct instructor access
- Cohort accountability & networking
- 24-hour code review turnaround
- 1-on-1 kickoff & graduation calls
- Certificate + alumni network
Founder's Edition
- Everything in Live Cohort
- 6 monthly 1-on-1 coaching calls
- Fractional CTO advisory and implementation support
- Custom learning path for your business
- Same-day code reviews
- Architecture consulting for your product
- Your proprietary model built with you
- Investor pitch coaching
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
11 transformative steps · 45 hours of hands-on content
Module 1: Getting Started with MLOps
6 lessons · Shu-Ha-Ri cycle
- Executive Overview: Why Most ML Projects Fail in Production
- The ML Life Cycle: Experimentation to Production
- Skills Needed for MLOps: Bridging Data Science and Engineering
- Build vs. Buy: When to Build Your Own Platform
- Tools and Infrastructure Overview
- Introducing the ML Projects You'll Build
Module 2: What is MLOps?
7 lessons · Shu-Ha-Ri cycle
- ML as a Continuous Loop: The Full Workflow
- Data Collection and Exploratory Analysis
- Modeling, Training, and Evaluation
- Deployment, Monitoring, and Maintenance
- Why Robust MLOps Matters for Business
- DevOps vs. MLOps: Key Differences
- MLOps Maturity Levels: Basic to Advanced
Module 3: Building Applications on Kubernetes
7 lessons · Shu-Ha-Ri cycle
- Docker Fundamentals: Writing Applications and Dockerfiles
- Building and Pushing Docker Images
- Kubernetes Architecture: Clusters, Nodes, and Pods
- Kubectl: Managing Kubernetes from the Command Line
- Kubernetes Objects: Deployments, Services, ConfigMaps
- Networking and Service Discovery
- Helm Charts: Package Management for Kubernetes
Module 4: Continuous Integration and Deployment
5 lessons · Shu-Ha-Ri cycle
- CI/CD for Machine Learning: Why It's Different
- GitLab CI: Automating Build and Test Pipelines
- Argo CD: GitOps for Kubernetes Deployments
- Prometheus and Grafana: Infrastructure Monitoring
- Building a Complete CI/CD Pipeline for ML
Module 5: Designing Reliable ML Systems
6 lessons · Shu-Ha-Ri cycle
- MLflow for Experiment Tracking: Logging Runs and Parameters
- Data Exploration Best Practices
- MLflow Model Registry: Versioning and Staging
- Feast as a Feature Store: Registering and Retrieving Features
- Feature Server and Feast UI
- Integrating Experiment Tracking with Feature Engineering
Module 6: Orchestrating ML Pipelines
6 lessons · Shu-Ha-Ri cycle
- Why Pipeline Orchestration Matters
- Kubeflow: The Task Orchestrator for ML
- Kubeflow Components: Building Reusable Pipeline Steps
- Building an Income Classifier Pipeline
- Passing Data Between Pipeline Components
- Scheduling and Monitoring Pipelines
Module 7: Productionizing ML Models
6 lessons · Shu-Ha-Ri cycle
- BentoML: A Platform for Model Deployment
- Building a Bento: Packaging Models for Production
- Deploying Bentos to Production
- Evidently for Data Drift Monitoring
- Building Drift Detection Dashboards
- Integrating Drift Detection into Kubeflow Pipelines
Module 8: Data Analysis and Preparation
6 lessons · Shu-Ha-Ri cycle
- Launching Notebook Servers in Kubeflow
- Workspace and Data Volume Management
- Creating Custom Kubeflow Notebook Images
- Data Passing: Simple Values vs. Large Datasets
- Project: Data Preparation for Object Detection
- Project: Data Preparation for Movie Recommender
Module 9: Model Training and Validation
7 lessons · Shu-Ha-Ri cycle
- Training Object Detection Models with YOLO
- Downloading Data with MinIO
- Creating Training and Validation Components
- Building and Executing Training Pipelines
- VolumeOp for Persistent Data Storage
- Tracking Training with TensorBoard
- Experiment Tracking and Model Registry with MLflow
Module 10: Model Inference and Serving
7 lessons · Shu-Ha-Ri cycle
- Why Model Deployment is Hard
- BentoML Services and Runners
- Loading Models with BentoML Runner
- Building Bentos for Deployment
- BentoML with MLflow Integration
- Creating Inference Services with MLflow
- KServe: An Alternative Serving Platform
Module 11: Monitoring and Explainability
7 lessons · Shu-Ha-Ri cycle
- Basic Monitoring: Metrics and Health Checks
- Custom Metrics for ML Systems
- Logging Best Practices for Production ML
- Alerting: When to Wake Someone Up
- Data Drift Detection in Production
- Model Explainability: Understanding Predictions
- Capstone: Your Complete ML Platform in Production
Production-Grade Tech Stack
Master the same tools used by OpenAI, Anthropic, and Google to build frontier AI systems
Frequently Asked Questions
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.
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.
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.
A standard laptop for development. We provide cloud setup instructions for running Kubernetes clusters. Local development uses Minikube or similar.
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.
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.
Self-paced · Lifetime access · 30-day guarantee
Start Your TransformationThis is not just education. This is technological sovereignty.