Building AI products and businesses requires making tough choices about what to build and how to go about it. I’ve heard of two styles: Ready, Aim, Fire and Ready, Fire, Aim

How much math do you need to know to be a machine learning engineer? It’s always nice to know more math! But there’s so much to learn that, realistically, it’s necessary to prioritize.

Since the pandemic started, several friends and teammates have shared with me privately that they were not doing well emotionally. I’m grateful to each person who trusted me enough to tell me this. How about you — are you doing okay?

In earlier letters, I discussed some differences between developing traditional software and AI products, including the challenges of unclear technical feasibility, complex product specification, and need for data to start development.

In a recent letter, I mentioned some challenges to building AI products. These problems are distinct from the issues that arise in building traditional software. They include unclear technical feasibility and complex product specification.

I’ve been following with excitement the recent progress in space launches. Earlier this week, Richard Branson and his Virgin Galactic team flew a rocket plane 53 miles up, earning him astronaut wings.

In a recent letter, I noted that one difference between building traditional software and AI products is the problem of complex product specification.

Last week, I mentioned that one difference between traditional software and AI products is the problem of unclear technical feasibility. In short, it can be hard to tell whether it’s practical to build a particular AI system.

With the rise of software engineering over several decades, many principles of how to build traditional software products and businesses are clear. But the principles of how to build AI products and businesses are still developing.

I’m thrilled to announce the first data-centric AI competition! I invite you to participate.For decades, model-centric AI competitions, in which the dataset is held fixed while you iterate on the code, have driven our field forward.

Around the world, students are graduating. If you’re one of them, or if someone close to you is graduating, congratulations!!! My family swapped pictures on WhatsApp recently and came across this one...

In school, most questions have only one right answer. But elsewhere, decisions often come down to a difficult choice among imperfect options. I’d like to share with you some approaches that have helped me make such decisions.

Benchmarks have been a significant driver of research progress in machine learning. But they've driven progress in model architecture, not approaches to building datasets, which can have a large impact on performance in practical applications.

I decided last weekend not to use a learning algorithm. Sometimes, a non-machine learning method works best.Now that my daughter is a little over two years old and highly mobile, I want to make sure the baby gate that keeps her away from the stairs is always shut.

So you’ve trained an accurate neural network model in a Jupyter notebook. You should celebrate! But . . . now what? Machine learning engineering in production is an emerging discipline that helps individual engineers and teams put models into the hands of users.