Letters
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
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
In a recent letter, I noted that one difference between building traditional software and AI products is the problem of complex product specification.
Letters
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
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
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
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
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
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.
Letters
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.
Letters
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...
Letters
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 move faster if we can shift from being model-centric to being data-centric.