By an engineer shipping 8.6M predictions a day · now live

AI Engineer? ML Engineer? Nobody Agrees What the Job Even Is.

It's software, data, machine learning, infrastructure, and increasingly hardware — all in one job. Most courses teach you how to build a model. I teach you how to ship it, scale it, monitor it, and keep it running.

From classical ML to LLM agents, learn the production skills behind systems serving 8.6M predictions a day and saving teams 1,000+ hours a week. For Python engineers who want to build AI that works in the real world.

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Getting Started is free. No card required.

The curriculum

Ship first. Go deeper next.

Ship a Machine Learning Product is live today and takes you the whole way — a product manager's ask to a service running in production. More courses follow as the curriculum grows.

Now live Pro

Ship a Machine Learning Product

Take a product manager's ask and a data scientist's notebook all the way to production: requirements with owners, a tested package, every prediction on record, the whole system under Docker, then a service running at a URL — and what to do the day it stops being right. Ten weeks.

Requirements→ Reproduce→ Package→ Trace→ Run locally→ Deploy→ Operate

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Coming soon Pro

Engineer AI Systems

Every guarantee you shipped in the first course assumed a model that answers the same way twice. This one asks what each of them becomes when the backend stops being deterministic — and how you prove a system is working when there is no single right answer to check against. Builds directly on Ship a Machine Learning Product.

Next up

Coming soon Pro

Build Your Own LLM

When calling an API is not enough: build the model, then serve it on your own compute — the architecture and the training, then the inference component, where it actually runs, how fast, and at what cost. Follows Engineer AI Systems.

Go deep

Coming soon

Get Interview-Ready

Machine learning engineering interviews: algorithms and data structures, machine learning coding, system design, and behavioral. Focused on exactly the fundamentals that show up at top companies.

Concept drills→ Coding from scratch→ System design→ Behavioral

Interview focused

Pricing

Ready to ship real applications?

That's what I built this for. The platform is early and I'm building it in the open — founding members fund the build and shape what ships next. If you're ready to ship real applications, sign up: $19.99 a month, yours for as long as you subscribe. After the founding window, prices go up.

Free

Getting Started, free forever. How the course works, and a live portfolio site deployed to GitHub Pages.

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Founding Founding Member

$19.99/ month

Full access to Ship a Machine Learning Product — a product manager's ask and a data scientist's notebook, taken to a service running in production over ten weeks.

Become a founding member

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Who is building this

Sarah Floris

Lead ML Engineer

My path into AI started in theoretical chemistry. At the University of Washington I worked on quantum mechanics simulations, Hamiltonian systems on computing clusters. That was my first experience building and debugging large computational systems.

When I moved into industry, I noticed the same problem everywhere. Teams could build models. Getting those models to run reliably in production was a completely different challenge. Pipelines failed. Inference systems broke. Models behaved unpredictably at scale.

That is the skill this curriculum teaches. Not just how to train models. How to ship systems that work in the real world.

Production experience

Built a real-time racing score model processing 8.6M predictions per day
Built large language model agents and integrations saving 1,080 hours per week across teams
Deployed time series models for fleet pricing on $3B in assets

Hugging Face contributor Microsoft QKit contributor 80K+ on LinkedIn 6.7K+ on Substack 2K+ on Medium