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, which was taken when I graduated from Carnegie Mellon (I’m standing in the middle).

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...

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.

It can take 6 to 24 months to bring a machine learning project from concept to deployment, but a specialized development platform can make things go much fasterMy team at Landing AI has been working on a platform called LandingLens...

How much data do you need to collect for a new machine learning project? If you’re working in a domain you’re familiar with, you may have a sense based on experience or from the literature. But when you’re working on a novel application, it’s hard to tell.

Last Sunday was my birthday. That got me thinking about the days leading to this one and those that may lie ahead.As a reader of The Batch, you’re probably pretty good at math. But let me ask you a question, and please answer from your gut, without calculating.

Machine learning development is highly iterative. Rather than designing a grand system, spending months to build it, and then launching it and hoping for the best, it’s usually better to build a quick-and-dirty system, get feedback, and use that feedback to improve the system.

Each year, the public relations agency Edelman produces a report on the online public’s trust in social institutions like government, media, and business. The latest Edelman Trust Barometer contains a worrisome finding: While technology was ranked the most trusted industry in the U.S.

I have a two-year-old daughter, and am expecting my son to be born later this week. When I think about what we can do to build a brighter future for our children, the most important thing is...

Earlier today, I spoke at a DeepLearning.AI event about MLOps, a field that aims to make building and deploying machine learning models more systematic. AI system development will...

Over the past weekend, I happened to walk by a homeless encampment and went over to speak with some of the individuals there. I spoke with...

Engineers need strong technical skills to be successful. But many underestimate the importance of developing strong communication skills as well. Many AI products are so...

One of the most important skills of an AI architect is the ability to identify ideas that are worth working on. Over the years, I’ve had fun applying machine learning to manufacturing, healthcare, climate change, agriculture, ecommerce, advertising, and...