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.

Choose your path

Four goals. One curriculum.

Every path shares the same rigorous foundations — and even the agentic systems in Path 2 need the classical models Path 1 teaches, for scoring, ranking, and routing. Where you go after that depends on what you are building.

Path 1 · Live Pro

Ship a Machine Learning Product

Build, deploy, and monitor a production machine learning system end to end. Carry one product from a messy notebook to a served API, then to monitoring and continuous retraining.

Python for ML→ Data Wrangling→ Numerical Foundations→ Classical ML→ Serving & APIs→ Deploy, Monitor, Retrain

Start the path →

Path 2 · Coming soon Pro

Build AI Systems

Ship AI-powered products by calling models rather than training them. Retrieval pipelines, agents, and language-model features built on top of the Anthropic API, the fastest route from idea to a working system.

Python for ML→ Data Wrangling→ Retrieval & RAG→ Agents→ Production

Fastest to ship

Path 3 · Coming soon

The Underlying Works

For engineers who want to understand what happens under the hood. Self-host, fine-tune, and train models at scale: pre-train a small language model, fine-tune with low-rank adaptation, and run multi-node training on GPUs.

The full ML curriculum→ Classical ML→ GPU Programming→ Distributed Training→ Models at Scale

Go deep

Path 4 · 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. Set up your environment, learn the git workflow, deploy a portfolio site.

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Founding Founding Member

$19.99/ month

Full access to Ship a Machine Learning Product — everything available today, and everything as it ships. New paths and the AI tutor coming soon, included for founding members the day they launch.

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