Letters
Toward Systematic Data Engineering
I’ve seen many new technologies go through a predictable process on their journey from idea to large scale adoption. First, a handful of experts apply their ideas intuitively.
Technical Insights
Letters
Making Software For a Heterogeneous World
The physical world is full of unique details that differ from place to place, person to person, and item to item. In contrast, the world of software is built on abstractions that make for relatively uniform coding environments and user...
Letters
Imaging Systems for Data-Centric AI Development
The image below shows two photos of the same gear taken under different conditions. From the point of view of a computer-vision algorithm — as well as the human eye — the imaging setup that produced the picture on the right makes...
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.
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.
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.
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.
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.
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.
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.
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.
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.