Welcome

Welcome — I am so glad you are here. Choosing to deepen your machine learning engineering skills is a meaningful step, and I am excited to guide you through this journey.

I started working in machine learning when the field looked very different from how it does today.

Back then, a lot of the focus was on data science: exploring datasets, building models, running experiments, and understanding what information could be extracted from data. That foundation is still incredibly important, and those skills remain at the heart of machine learning.

But over time, the nature of the work changed. Models became only one part of a much larger system.

As AI applications grew — especially with the rise of large language models — the engineering challenges around those models became just as important as the models themselves.

How do you build systems that are reliable? How do you deploy them safely? How do you monitor them once they are running? How do you handle changing data, unexpected behavior, failures, and the many decisions that happen between a prototype and something people actually use?

Those were the skills I found myself learning through experience.

Over the past several years, I have built and operated machine learning systems in production, led engineering teams, and worked on the infrastructure needed to make these types of systems reliable for real users. I have worked on real-time machine learning platforms, data pipelines processing hundreds of gigabytes daily, and deployment and monitoring systems supporting production workloads.

Who it is for

These courses are for engineers who know some Python and want to learn how to build and ship machine learning systems.

You do not need previous machine learning experience. We will build the foundations step by step and gradually move into more advanced engineering decisions: understanding tradeoffs, designing reliable systems, and thinking about how software behaves in the real world.

You should be comfortable working in a terminal, reading documentation, and learning through hands-on practice.

If you can run a command, investigate an error, and stay curious when something does not work, these will be the courses for you.

Four Courses

I am building four courses. Each one stands on the last, and the first is live today.

  1. 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. Start here.
  2. Engineer AI Systems. The system around the model — starting with the API both ways: an LLM call, or a FastAPI endpoint over your trained model, under the same discipline. Coming soon.
  3. Build Your Own LLM. When calling an API is not enough: your own LLM on your own compute — where the model actually runs, how fast, and at what cost. Coming soon.
  4. Get Interview-Ready. The fundamentals that show up in machine learning engineering interviews: algorithms and data structures, machine learning coding, system design, and behavioral. Coming soon.

Getting started

Two short lessons remain before the first course begins.

The next one, How the Work Works, explains the quizzes, projects, and capstone in detail. Then Your Portfolio Site helps you set up a place to publish the work you are about to build.

Once those are complete, you will be ready to begin.