Cartoon about data
Letters

Developing AI Products Part 5: Data Drift, Concept Drift, and Other Maintenance Issues

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.
Series of spreadsheets with different data
Letters

Developing AI Products Part 4: Getting 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.
Andrew Ng sitting in the Blue Origin passenger capsule
Letters

The New Space Race

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.
Cartoon about traditional software and AI
Letters

Developing AI Products Part 3: Coping With Product Specification

In a recent letter, I noted that one difference between building traditional software and AI products is the problem of complex product specification.
Magnifying glass over the words Unclear, Technical and Feasibility
Letters

Developing AI Products Part 2: How To Assess Technical Feasibility

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.
Slide with information about challenges to building AI products and businesses
Letters

Developing AI Products Part 1: How AI Product Development is Different From Traditional Software

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.
Data-Centric AI Competition slide
Letters

Ready, Set, Improve the Data!

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.
Andrew Ng and his family on his graduation day
Letters

Congratulations to all the Graduates!

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...
Photograph of a two-way road in the woods
Letters

How to Make Tough Decisions

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.
Conventional and data-centric benchmarks
Letters

Data-Centric AI Development: A New Kind of Benchmark

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.
Alert door widget
Letters

When Not to Use Machine Learning

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.
Pictures of Robert Crowe, Andrew Ng and Laurence Moroney (from left to right)
Letters

Introducing the Machine Learning Engineering for Production (MLOps) Specialization

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.
Machine Learning project lifecycle
Letters

Data-Centric-AI Development: The Platform Approach

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 faster.My team at Landing AI has been working on a platform called LandingLens for efficiently building computer vision models.
Iteration workflow
Letters

Data-Centric AI Development, Part 3: Limit Data Collection Time

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.
Calendar pages flying away
Letters

Make Every Day Count

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.

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