Most ML engineers never ship anything.
They can follow tutorials.
They can train models.
They can call APIs.
But when it comes to deploying systems that actually run in production…
They get stuck.
87% of machine learning projects never reach production.
Not because the ideas are bad.
Because building production systems is a completely different skill.
The Gap
Most courses teach:
- Machine learning concepts
- Theory
- Toy notebooks
- Kaggle-style workflows
But production systems require something else:
- Debugging pipelines
- Designing infrastructure
- Deploying models
- Maintaining systems that run every day
That's the gap.
And it's exactly the gap employers pay six figures to solve.
Sarah Floris
Senior ML Engineer
My path into artificial intelligence started somewhere unexpected.
Theoretical chemistry.
At the University of Washington, I worked on quantum mechanics simulations, running Hamiltonian systems on high-performance computing clusters.
It was my first experience building and debugging large computational systems.
During graduate school I joined DIRECT, a program focused on data-intensive research and data science.
That's where I started working seriously with machine learning.
But when I transitioned into industry, I noticed the same problem everywhere.
Teams could build models.
But getting those models to run reliably in production was a completely different challenge.
Pipelines failed.
Inference systems broke.
Models behaved unpredictably at scale.
Over time, I learned how to build machine learning systems that actually run in production.
That's the skill this program teaches.
Not just how to train models.
But how to ship systems that work in the real world.
- Built a real-time racing score model processing 8.6M predictions/day
- Built LLM agents and integrations saving 1,080 hours/week across teams
- Deployed time series models (ARIMA) for fleet pricing on $3B in assets
- And many more production systems
The Program
This program exists for one reason:
To teach engineers how to ship AI systems.
Not just experiment.
Not just train models.
Ship them.
By the end of the program, you'll know how to:
- Deploy large language model systems
- Build retrieval-augmented generation pipelines
- Design AI agent workflows
- Debug broken ML pipelines
- Run production inference systems
What You Build
During the program you'll build and deploy real systems.
Projects include:
- A production LLM application
- A retrieval-augmented generation system
- A fine-tuned model
- An AI agent workflow
- A production capstone project
These are systems you can show employers.
Not tutorials. Not notebooks. Real deployments.
What You Get
6 Guided Modules
Every lesson ends with something deployed or debugged.
Debug Challenges
Real bugs. Real fixes. The skill that separates good engineers from great ones.
AI Mock Interviews
Practice system design and ML concepts. Unlimited attempts.
6 One-on-One Sessions Early Bird Only
Mock interviews. Resume review. Debugging help. Career strategy.
Early bird closes when all spots are filled.
After that, $1,499.
- Full curriculum (6 modules)
- Lifetime access + updates
- AI mock interviews
- 6 one-on-one sessions with me (early bird only)
14-day money-back guarantee. No questions asked.
Questions
The difference between ML engineers who struggle and those who succeed is simple:
Some learn concepts.
Others learn how to ship systems.
This program is for the engineers who want to be in the 13% that ship.
Reserve Your Spot →