Magnifying glass over the words Unclear, Technical and Feasibility
Technical Insights

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
Technical Insights

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
Technical Insights

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.
Conventional and data-centric benchmarks
Technical Insights

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
Technical Insights

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.
Machine Learning project lifecycle
Technical Insights

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
Technical Insights

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.
Lifecycle of an Machine Learning project
Technical Insights

Iteration in AI Development

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...
System explaining an AI system
Technical Insights

Data-Centric AI Development, Part 2: A Critical Shift in Perspective

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.
How to scope AI projects slide
Technical Insights

Five Steps to Scoping AI Projects

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 other industries.
Process of diagnosing a medical patient slide
Technical Insights

Choose the Right Point On the Automation Spectrum

AI-enabled automation is often portrayed as a binary on-or-off: A process is either automated or not. But in practice, automation is a spectrum, and AI teams have to choose where on this spectrum to operate.
A/B Test loop for building human insight
Technical Insights

A Different Approach to A/B Testing

When a lot of data is available, machine learning is great at automating decisions. But when data is scarce, consider using the data to augment human insight, so people can make better decisions.
Speech bubble that says "It did well on the test set!"
Technical Insights

High Test-Set Accuracy Is Not Enough

Over the last several decades, driven by a multitude of benchmarks, supervised learning algorithms have become really good at achieving high accuracy on test datasets. As valuable as this is, unfortunately maximizing average test set accuracy isn’t always enough.
Slide that says "Proof of concept - production"
Technical Insights

Don't Confuse Proof of Concept With Production Deployment

Last week, I talked about how best practices for machine learning projects are not one-size-fits-all, and how they vary depending on whether a project uses structured or unstructured data, and whether the dataset is small or big.
Table with information related to data and datasets
Technical Insights

Structured and Unstructured Data: Implications for AI Development

Experience gained in building a model to solve one problem doesn’t always transfer to building models for other problems. How can you tell whether or not intuitions honed in one project are likely to generalize to another?

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