Stop calling AI APIs.
Start building AI systems.
Most courses teach you to call an API.
This one teaches you how to run, fine-tune, deploy, and understand modern ML systems.
From local open-source LLMs to production ML infrastructure.
Built by Sarah Floris — Senior ML Platform Engineer with 8+ years building ML systems in production.
Followed by 80K+ engineers on LinkedIn.
Founding members save $600
Only 20 founding spots available.
- Full course (12 modules) — lifetime access
- Help shape the curriculum as a founding member
- Private engineering Discord community
- Lifetime updates as the AI ecosystem evolves
Not ready to commit? Join the waitlist
Engineers following Sarah's work are at
What You'll Build
You will run real open-source LLMs, fine-tune them on your data, and deploy them into production systems.
Fine-Tune an LLM
Train and adapt a model on your own dataset.
Resume Chatter
Build an AI agent that answers questions about your resume.
Production Inference Pipeline
Serve models reliably outside of notebooks.
Production ML System Capstone
Ship a full ML system from training to deployment.
These are projects you actually own — not tutorial clones.
What You'll Be Able To Do
By the end of the course, you'll understand how modern AI systems actually work.
- Run and optimize LLMs instead of relying on hosted APIs
- Deploy ML models to production environments
- Design AI agents that work in real workflows
- Fine-tune models on your own datasets
- Understand model constraints (parameters, KV cache, VRAM, throughput)
- Estimate the real infrastructure cost of ML systems
- Confidently approach ML system design interviews
Why These Skills Matter
Companies aren't just hiring people who can call AI APIs.
They need engineers who understand:
- how models run
- how to fine-tune them
- how to deploy them
- how to operate ML systems in production
These roles command some of the highest salaries in software engineering.
Typical US compensation ranges:
- ML Engineer: $150K – $200K
- Senior ML Engineer: $180K – $250K+
This course focuses on the engineering side of AI systems — the skills used in real production environments.
Who This Course Is For
Engineers who want to go beyond tutorials and understand how modern AI systems actually work.
Great fit for:
- Software engineers transitioning into ML / AI
- Data scientists who want to deploy models
- Engineers curious how LLM systems work under the hood
- Builders who want to run and fine-tune models
Not for:
- People looking for a quick certificate
- Passive video watchers who don't want to code
- Anyone expecting copy-paste tutorials
The Curriculum
12 rigorous modules covering the full path from Python fundamentals to production ML systems.
Every lesson includes:
- Worked examples
- Hands-on exercises
- Debugging challenges
- Quizzes and flashcards
Your Instructor
Sarah Floris
Senior ML Platform Engineer
I've spent 8+ years building ML systems that run in production at scale. Not demos. Not toy projects. Real systems serving millions of daily inferences.
Frequently Asked Questions
Ready to build real AI systems?
20 founding engineer spots available. $1,199 $1,799 at launch.
Reserve Your Founding Member Spot →